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Update app.py
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app.py
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import gradio as gr
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import google.
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import os
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import mimetypes
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from PIL import Image
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import
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import
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"gemini-1.5-flash-latest",
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"gemini-1.5-pro-latest",
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"gemini-1.0-pro",
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"gemini-pro-vision", # Example vision model
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# "gemini-experimental", # Add other relevant models
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]
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#
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"gemini-
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# Safety settings could be added here too (as dropdowns or checkboxes)
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},
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"gemini-1.5-pro-latest": {
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# Similar params, possibly different defaults or ranges
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"temperature": {"type": "slider", "min": 0.0, "max": 2.0, "step": 0.1, "default": 1.0},
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"top_p": {"type": "slider", "min": 0.0, "max": 1.0, "step": 0.01, "default": 0.95},
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"top_k": {"type": "slider", "min": 1, "max": 100, "step": 1, "default": 40},
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"max_output_tokens": {"type": "number", "min": 1, "step": 1, "default": 8192},
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"stop_sequences": {"type": "textbox", "lines": 1, "placeholder": "e.g., END,STOP", "default": ""},
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},
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"gemini-1.0-pro": {
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# Params for older model might differ slightly
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"temperature": {"type": "slider", "min": 0.0, "max": 1.0, "step": 0.1, "default": 0.9}, # Different max/default maybe
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"top_p": {"type": "slider", "min": 0.0, "max": 1.0, "step": 0.01, "default": 0.95},
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"top_k": {"type": "slider", "min": 1, "max": 100, "step": 1, "default": 40},
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"max_output_tokens": {"type": "number", "min": 1, "step": 1, "default": 2048}, # Different default
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"stop_sequences": {"type": "textbox", "lines": 1, "placeholder": "e.g., END,STOP", "default": ""},
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},
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"gemini-pro-vision": {
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# Vision models might have fewer text-generation params or different ones
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"temperature": {"type": "slider", "min": 0.0, "max": 1.0, "step": 0.1, "default": 0.4},
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"top_p": {"type": "slider", "min": 0.0, "max": 1.0, "step": 0.01, "default": 0.95},
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"top_k": {"type": "slider", "min": 1, "max": 100, "step": 1, "default": 32},
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"max_output_tokens": {"type": "number", "min": 1, "step": 1, "default": 2048},
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# No stop sequences typically needed here? Check docs.
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}
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}
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#
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def get_mime_type(file_path):
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"""Get MIME type using python-magic for reliability."""
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try:
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mime = magic.Magic(mime=True)
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return mime.from_file(file_path)
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except Exception:
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# Fallback to mimetypes if magic fails
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return mimetypes.guess_type(file_path)[0]
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def convert_file_to_text(file_obj):
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"""
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Attempts to convert various file types to text.
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Returns (text_content, original_filename) or (None, original_filename) if conversion fails.
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"""
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file_path = file_obj.name
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filename = os.path.basename(file_path)
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mime_type = get_mime_type(file_path)
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print(f"Processing file: {filename}, MIME type: {mime_type}") # Debugging
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try:
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if mime_type is None:
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# If MIME type is unknown, try reading as text
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print(f"Warning: Unknown MIME type for {filename}. Attempting to read as text.")
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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return f.read(), filename
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elif mime_type.startswith("text/"):
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with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
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return f.read(), filename
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elif mime_type == "application/pdf":
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# Placeholder for PDF conversion (requires pypdf or similar)
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print(f"PDF conversion not implemented yet for {filename}.")
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# from pypdf import PdfReader # Example
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# reader = PdfReader(file_path)
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# text = ""
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# for page in reader.pages:
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# text += page.extract_text() + "\n"
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# return text, filename
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return f"[Unsupported PDF: {filename} - Conversion not implemented]", filename # Temporary
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elif mime_type in ["application/msword", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"]:
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# Placeholder for DOCX conversion (requires python-docx or similar)
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print(f"DOCX conversion not implemented yet for {filename}.")
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# import docx # Example
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# doc = docx.Document(file_path)
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# text = "\n".join([para.text for para in doc.paragraphs])
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# return text, filename
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return f"[Unsupported Word Doc: {filename} - Conversion not implemented]", filename # Temporary
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else:
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# For other unsupported types, return a marker
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print(f"Unsupported file type: {mime_type} for {filename}. Skipping content.")
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return f"[Unsupported file type: {mime_type} - {filename}]", filename
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except Exception as e:
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print(f"Error converting file {filename}: {e}")
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return f"[Error converting file: {filename}]", filename
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def prepare_gemini_input(prompt, files):
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"""Prepares the input list for Gemini, handling text and images."""
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gemini_parts = []
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if prompt:
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gemini_parts.append(prompt)
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if files:
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for file_obj in files:
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file_path = file_obj.name
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mime_type = get_mime_type(file_path)
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filename = os.path.basename(file_path)
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print(f"Preparing file for Gemini: {filename}, MIME: {mime_type}")
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if mime_type and mime_type.startswith("image/"):
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try:
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img = Image.open(file_path)
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# Convert image to bytes (e.g., PNG or JPEG)
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# Gemini API directly accepts PIL Images usually
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gemini_parts.append(img)
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print(f"Added image: {filename}")
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except Exception as e:
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print(f"Error processing image {filename}: {e}")
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gemini_parts.append(f"[Error processing image: {filename}]")
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elif mime_type and mime_type.startswith("video/"): # Gemini 1.5 Pro can handle video
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# Upload file via File API first (more complex, needs google.ai.generativelanguage)
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# For simplicity here, we'll just note it's a video
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# or provide a basic text representation if conversion isn't implemented
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print(f"Video file detected: {filename}. Full video processing requires File API.")
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gemini_parts.append(f"[Video file: {filename} - Requires File API upload]")
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# Placeholder: Add text conversion if feasible for your use case
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# text_content, _ = convert_file_to_text(file_obj)
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# if text_content:
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# gemini_parts.append(f"--- Content of video file {filename} (extracted as text) ---\n{text_content}")
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elif mime_type and mime_type.startswith("audio/"): # Gemini 1.5 Pro can handle audio
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print(f"Audio file detected: {filename}. Full audio processing requires File API.")
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gemini_parts.append(f"[Audio file: {filename} - Requires File API upload]")
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# Placeholder: Add text conversion if feasible (e.g. transcript)
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# text_content, _ = convert_file_to_text(file_obj) # Needs specific audio-to-text logic
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# if text_content:
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# gemini_parts.append(f"--- Content of audio file {filename} (extracted as text) ---\n{text_content}")
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else: # Assume text or convertible to text
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text_content, original_filename = convert_file_to_text(file_obj)
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if text_content:
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# Add context marker
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gemini_parts.append(f"\n--- Content from file: {original_filename} ---\n{text_content}\n--- End of file: {original_filename} ---")
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else:
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gemini_parts.append(f"[Could not process file: {original_filename}]")
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# Ensure there's at least one part (maybe an empty string if only files were given?)
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if not gemini_parts:
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gemini_parts.append("") # Avoid sending empty list
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return gemini_parts
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# --- Gradio UI Functions ---
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def validate_api_key(api_key):
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"""Checks if the API key is potentially valid by trying to list models."""
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if not api_key:
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return "<p style='color: orange;'>Please enter an API Key.</p>"
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try:
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genai.
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models
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available_core_models = [m.name for m in models if 'generateContent' in m.supported_generation_methods]
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if any(model_name.split('/')[-1] in AVAILABLE_MODELS for model_name in available_core_models):
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return "<p style='color: green;'>API Key seems valid (can list models).</p>"
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else:
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return "<p style='color: orange;'>API Key is valid but might not have access to the required Gemini models.</p>"
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except Exception as e:
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else:
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label=key.replace("_", " ").title(), # Nicer label
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value=param_config.get("default") # Set default value
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# Add specific updates for slider ranges etc. if needed
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# minimum=param_config.get("min"),
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# maximum=param_config.get("max"),
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# step=param_config.get("step")
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)
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else:
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# Parameter does NOT exist for this model: hide it
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updates[param_elements[key]] = gr.update(visible=False, value=None) # Reset value when hiding
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return updates
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def handle_chat(api_key, model_name, history, message, files, *params_tuple):
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"""Handles the chat interaction."""
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# 1. Basic Validation
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if not api_key:
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gr.Warning("Gemini API Key is missing!")
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return history, "" # Return unchanged history and empty textbox
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if not message and not files:
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gr.Warning("Please enter a message or upload files.")
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return history, ""
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# 2. Configure API Key
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try:
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genai.configure(api_key=api_key)
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except Exception as e:
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gr.Error(f"Failed to configure API Key: {e}")
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return history, message # Keep message in textbox for retry
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# 3. Prepare Generation Config from *params_tuple
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param_keys = [key for key, config in MODEL_PARAMS.get(model_name, {}).items()]
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generation_config_dict = {}
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if len(params_tuple) == len(param_keys):
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generation_config_dict = {key: val for key, val in zip(param_keys, params_tuple) if val is not None}
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# Handle stop sequences (expecting comma-separated string)
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if 'stop_sequences' in generation_config_dict and isinstance(generation_config_dict['stop_sequences'], str):
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sequences = [s.strip() for s in generation_config_dict['stop_sequences'].split(',') if s.strip()]
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if sequences:
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generation_config_dict['stop_sequences'] = sequences
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else:
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del generation_config_dict['stop_sequences'] # Remove if empty/invalid
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print(f"Using Generation Config: {generation_config_dict}") # Debug
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else:
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#
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# safety_settings = {...}
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model = genai.GenerativeModel(model_name)#, safety_settings=safety_settings)
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# Convert Gradio history (list of lists) to Gemini format (list of Content objects)
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gemini_history = []
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for user_msg, model_msg in history:
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# Simple text history for now. Need enhancement for multimodal history.
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if user_msg: gemini_history.append({'role': 'user', 'parts': [user_msg]})
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if model_msg: gemini_history.append({'role': 'model', 'parts': [model_msg]})
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chat = model.start_chat(history=gemini_history)
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print(f"Starting chat with history (simplified): {gemini_history}") # Debugging
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except Exception as e:
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gr.Error(f"Failed to initialize model or chat: {e}")
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return history, message # Keep message in textbox
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# 6. Send Message and Get Response
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response_text = ""
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try:
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# Use streaming for better UX in chat
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response = chat.send_message(gemini_input_parts,
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generation_config=genai.types.GenerationConfig(**generation_config_dict),
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stream=True)
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full_response_content = ""
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for chunk in response:
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# Check if the chunk has text content
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if hasattr(chunk, 'text'):
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chunk_text = chunk.text
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print(f"Stream chunk: {chunk_text}") # Debug stream
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full_response_content += chunk_text
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# Yield intermediate updates to the chatbot
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current_history = history + [[message or "[Input files only]", full_response_content]]
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yield current_history, "" # Update chatbot, clear input
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# Check for image data if model supports it (more complex parsing needed)
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# elif chunk.parts and chunk.parts[0].inline_data:
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# # Handle potential image output - requires modification
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# pass
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response_text = full_response_content # Final text response
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# Check for blocked prompts or safety issues
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if not response_text and response.prompt_feedback.block_reason:
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block_reason = response.prompt_feedback.block_reason
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safety_ratings = response.prompt_feedback.safety_ratings
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gr.Warning(f"Request blocked. Reason: {block_reason}. Ratings: {safety_ratings}")
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# Append a notice to history instead of an empty response
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history.append([message or "[Input files only]", f"[Request blocked due to: {block_reason}]"])
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return history, "" # Clear input box
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except Exception as e:
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gr.Error(f"Error during generation: {e}")
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# Optionally add the error to history for context
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history.append([message or "[Input files only]", f"[Error during generation: {str(e)}]"])
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return history, "" # Clear input box
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# 7. Update History and Clear Input
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# The yielding above handles intermediate updates. This is the final state.
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final_history = history + [[message or "[Input files only]", response_text or "[No text content received]"]]
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return final_history, "" # Final update, clear input
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def handle_single_response(api_key, model_name, prompt, files, *params_tuple):
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"""Handles the single response interaction."""
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# 1. Validations
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if not api_key:
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gr.Warning("Gemini API Key is missing!")
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return "[Error: API Key Missing]", None # Text output, Image output
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if not prompt and not files:
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gr.Warning("Please enter a prompt or upload files.")
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return "[Error: No input provided]", None
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# 2. Configure API Key
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try:
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except Exception as e:
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return
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else:
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del generation_config_dict['stop_sequences']
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print(f"Using Generation Config: {generation_config_dict}") # Debug
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else:
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model = genai.GenerativeModel(model_name)
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except Exception as e:
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gr.Error(f"Failed to initialize model: {e}")
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return f"[Error: Model Initialization Failed: {e}]", None
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# 6. Generate Content (Non-streaming for single response usually)
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output_text = "[No text content generated]"
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output_image = None # Placeholder for image output
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try:
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response =
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389 |
-
|
390 |
-
|
391 |
-
|
392 |
-
|
393 |
-
|
394 |
-
|
395 |
-
|
396 |
-
|
397 |
-
|
398 |
-
|
399 |
-
|
400 |
-
# This part needs refinement based on how Gemini API returns mixed content
|
401 |
-
# For now, prioritize text and assume first image part if present
|
402 |
-
response_text_parts = []
|
403 |
-
for part in response.parts:
|
404 |
-
if hasattr(part, 'text'):
|
405 |
-
response_text_parts.append(part.text)
|
406 |
-
elif hasattr(part, 'inline_data') and part.inline_data.mime_type.startswith('image/'):
|
407 |
-
if output_image is None: # Display the first image found
|
408 |
-
try:
|
409 |
-
image_data = part.inline_data.data
|
410 |
-
img = Image.open(io.BytesIO(image_data))
|
411 |
-
output_image = img
|
412 |
-
print("Image received in response.")
|
413 |
-
except Exception as img_err:
|
414 |
-
print(f"Error decoding image from response: {img_err}")
|
415 |
-
response_text_parts.append("[Error decoding image in response]")
|
416 |
-
|
417 |
-
if response_text_parts:
|
418 |
-
output_text = "\n".join(response_text_parts)
|
419 |
-
elif hasattr(response, 'text'): # Fallback if parts parsing fails but text attribute exists
|
420 |
-
output_text = response.text
|
421 |
-
|
422 |
-
# Check if only an image was returned (or intended)
|
423 |
-
if not response_text_parts and output_image is not None:
|
424 |
-
output_text = "[Image generated - see output below]"
|
425 |
-
|
426 |
-
|
427 |
except Exception as e:
|
428 |
-
|
429 |
-
output_text = f"[Error during generation: {str(e)}]"
|
430 |
-
|
431 |
-
# 7. Return results
|
432 |
-
return output_text, output_image
|
433 |
-
|
434 |
-
|
435 |
-
# --- Build Gradio Interface ---
|
436 |
-
with gr.Blocks(theme=gr.themes.Soft()) as demo:
|
437 |
-
gr.Markdown("# Gemini API Interface")
|
438 |
-
gr.Markdown("Interact with Google Gemini models using your own API key. Supports chat, single responses, file uploads, and model-specific parameters.")
|
439 |
|
|
|
|
|
440 |
# API Key Section
|
441 |
-
|
442 |
-
|
443 |
-
|
444 |
-
|
445 |
-
|
446 |
-
|
447 |
-
|
448 |
-
|
449 |
-
|
450 |
-
|
451 |
-
|
452 |
-
|
453 |
-
|
454 |
-
|
455 |
-
|
456 |
-
|
457 |
-
|
458 |
-
|
459 |
-
|
460 |
-
|
461 |
-
|
462 |
-
# They will be shown/hidden by the update_parameter_visibility function
|
463 |
-
all_possible_params = set(k for params in MODEL_PARAMS.values() for k in params)
|
464 |
-
for param_name in sorted(list(all_possible_params)): # Sort for consistent order
|
465 |
-
# Determine control type based on the first model that defines it (can be refined)
|
466 |
-
control_type = "textbox" # Default
|
467 |
-
config = {}
|
468 |
-
for model_cfg in MODEL_PARAMS.values():
|
469 |
-
if param_name in model_cfg:
|
470 |
-
config = model_cfg[param_name]
|
471 |
-
control_type = config.get("type", "textbox")
|
472 |
-
break # Found config for this param
|
473 |
-
|
474 |
-
if control_type == "slider":
|
475 |
-
param_elements[param_name] = gr.Slider(
|
476 |
-
label=param_name.replace("_", " ").title(),
|
477 |
-
minimum=config.get("min", 0),
|
478 |
-
maximum=config.get("max", 1),
|
479 |
-
step=config.get("step", 0.1),
|
480 |
-
value=config.get("default"),
|
481 |
-
visible=False, # Initially hidden
|
482 |
-
interactive=True
|
483 |
-
)
|
484 |
-
elif control_type == "number":
|
485 |
-
param_elements[param_name] = gr.Number(
|
486 |
-
label=param_name.replace("_", " ").title(),
|
487 |
-
minimum=config.get("min", 1),
|
488 |
-
step=config.get("step", 1),
|
489 |
-
value=config.get("default"),
|
490 |
-
visible=False,
|
491 |
-
interactive=True
|
492 |
-
)
|
493 |
-
else: # Default to Textbox for stop_sequences etc.
|
494 |
-
param_elements[param_name] = gr.Textbox(
|
495 |
-
label=param_name.replace("_", " ").title(),
|
496 |
-
lines=config.get("lines", 1),
|
497 |
-
placeholder=config.get("placeholder", ""),
|
498 |
-
value=config.get("default", ""),
|
499 |
-
visible=False,
|
500 |
-
interactive=True
|
501 |
-
)
|
502 |
-
|
503 |
-
# Pack the parameter components into a list for function inputs/outputs
|
504 |
-
# IMPORTANT: The order here MUST match the order expected by handle_chat/handle_single_response
|
505 |
-
ordered_param_components = [param_elements[key] for key in sorted(param_elements.keys())]
|
506 |
-
|
507 |
-
|
508 |
-
# Main Interaction Area (Tabs)
|
509 |
-
with gr.Tabs():
|
510 |
-
# --- Chat Interface Tab ---
|
511 |
-
with gr.TabItem("Chat Interface"):
|
512 |
-
gr.Markdown("Have a conversation with the selected model. Upload files to include their content.")
|
513 |
-
chat_history_state = gr.State([]) # Holds the conversation history
|
514 |
-
chatbot_display = gr.Chatbot(label="Conversation", height=500)
|
515 |
-
with gr.Row():
|
516 |
-
chat_file_upload = gr.File(label="Upload Files (Text, Images, etc.)", file_count="multiple")
|
517 |
-
with gr.Row():
|
518 |
-
chat_message_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", scale=4, lines=3)
|
519 |
-
chat_submit_button = gr.Button("Send", variant="primary", scale=1)
|
520 |
-
clear_chat_button = gr.Button("Clear Chat History")
|
521 |
-
|
522 |
-
|
523 |
-
# --- Single Response Tab ---
|
524 |
with gr.TabItem("Single Response"):
|
525 |
-
gr.
|
526 |
-
|
527 |
-
|
528 |
-
|
529 |
-
|
530 |
-
|
531 |
-
|
532 |
-
|
533 |
-
|
534 |
-
|
535 |
-
|
536 |
-
|
537 |
-
|
538 |
-
|
539 |
-
|
540 |
-
validate_button.click(
|
541 |
-
fn=validate_api_key,
|
542 |
inputs=[api_key_input],
|
543 |
-
outputs=[
|
544 |
-
)
|
545 |
-
|
546 |
-
# 2. Update Parameters UI when Model Changes
|
547 |
-
model_dropdown.change(
|
548 |
-
fn=update_parameter_visibility,
|
549 |
-
inputs=[model_dropdown],
|
550 |
-
outputs=list(param_elements.values()) # Pass the actual components
|
551 |
-
)
|
552 |
-
|
553 |
-
# Trigger initial parameter visibility update on load
|
554 |
-
demo.load(
|
555 |
-
fn=update_parameter_visibility,
|
556 |
-
inputs=[model_dropdown],
|
557 |
-
outputs=list(param_elements.values())
|
558 |
-
)
|
559 |
-
|
560 |
-
# 3. Chat Submission Logic (using .then() for streaming if possible, or standard submit)
|
561 |
-
# Note: Gradio streaming with gr.Chatbot often uses yields
|
562 |
-
chat_submit_button.click(
|
563 |
-
fn=handle_chat,
|
564 |
-
inputs=[
|
565 |
-
api_key_input,
|
566 |
-
model_dropdown,
|
567 |
-
chat_history_state,
|
568 |
-
chat_message_input,
|
569 |
-
chat_file_upload
|
570 |
-
] + ordered_param_components, # Add dynamic params
|
571 |
-
outputs=[chatbot_display, chat_message_input] # Update chatbot, clear input box
|
572 |
-
).then(
|
573 |
-
# Update the state *after* the response is fully generated
|
574 |
-
lambda history: history, # Simple pass-through to get final history
|
575 |
-
inputs=chatbot_display,
|
576 |
-
outputs=chat_history_state
|
577 |
)
|
578 |
-
|
579 |
-
|
580 |
-
|
581 |
-
|
582 |
-
|
583 |
-
|
584 |
-
chat_history_state,
|
585 |
-
chat_message_input,
|
586 |
-
chat_file_upload
|
587 |
-
] + ordered_param_components,
|
588 |
-
outputs=[chatbot_display, chat_message_input]
|
589 |
-
).then(
|
590 |
-
lambda history: history,
|
591 |
-
inputs=chatbot_display,
|
592 |
-
outputs=chat_history_state
|
593 |
-
)
|
594 |
-
|
595 |
-
|
596 |
-
# 4. Clear Chat Logic
|
597 |
-
def clear_chat_history_func():
|
598 |
-
return [], [] # Clears chatbot display and history state
|
599 |
-
|
600 |
-
clear_chat_button.click(
|
601 |
-
fn=clear_chat_history_func,
|
602 |
-
inputs=[],
|
603 |
-
outputs=[chatbot_display, chat_history_state]
|
604 |
)
|
605 |
-
|
606 |
-
#
|
607 |
-
|
608 |
-
|
609 |
-
inputs=[
|
610 |
-
|
611 |
-
model_dropdown,
|
612 |
-
single_prompt_input,
|
613 |
-
single_file_upload
|
614 |
-
] + ordered_param_components, # Add dynamic params
|
615 |
-
outputs=[single_output_text, single_output_image]
|
616 |
)
|
617 |
|
618 |
-
|
619 |
-
# Launch the Gradio app
|
620 |
-
if __name__ == "__main__":
|
621 |
-
demo.launch(debug=True) # Set debug=False for deployment
|
|
|
1 |
import gradio as gr
|
2 |
+
import google.genai as genai
|
|
|
|
|
3 |
from PIL import Image
|
4 |
+
import os
|
5 |
+
import textract
|
6 |
+
|
7 |
+
# List of available models (including experimental and recent ones)
|
8 |
+
models = [
|
9 |
+
"gemini-2.5-flash-preview-04-17",
|
10 |
+
"gemini-2.5-pro-preview-03-25",
|
11 |
+
"gemini-2.0-flash",
|
12 |
+
"gemini-2.0-flash-lite",
|
13 |
+
"gemini-2.0-flash-thinking-exp-01-21",
|
14 |
+
"gemini-1.5-pro",
|
15 |
+
"gemini-2.0-flash-exp-image-generation"
|
|
|
|
|
|
|
|
|
|
|
16 |
]
|
17 |
|
18 |
+
# Model types for handling inputs
|
19 |
+
model_types = {
|
20 |
+
"gemini-2.5-flash-preview-04-17": "text",
|
21 |
+
"gemini-2.5-pro-preview-03-25": "text",
|
22 |
+
"gemini-2.0-flash": "text",
|
23 |
+
"gemini-2.0-flash-lite": "text",
|
24 |
+
"gemini-2.0-flash-thinking-exp-01-21": "text",
|
25 |
+
"gemini-1.5-pro": "text",
|
26 |
+
"gemini-2.0-flash-exp-image-generationn": "multimodal"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
27 |
}
|
28 |
|
29 |
+
# Function to validate API key
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
30 |
def validate_api_key(api_key):
|
|
|
|
|
|
|
31 |
try:
|
32 |
+
client = genai.Client(api_key=api_key)
|
33 |
+
client.models.list() # Validate by attempting to list models
|
34 |
+
return True, "API Key is valid."
|
|
|
|
|
|
|
|
|
|
|
|
|
35 |
except Exception as e:
|
36 |
+
return False, f"Invalid API Key: {str(e)}"
|
37 |
+
|
38 |
+
# Function to process uploaded files
|
39 |
+
def process_files(files, model_type):
|
40 |
+
inputs = []
|
41 |
+
for file_path in files:
|
42 |
+
if model_type == "multimodal" and file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):
|
43 |
+
img = Image.open(file_path)
|
44 |
+
inputs.append(img)
|
45 |
else:
|
46 |
+
try:
|
47 |
+
text = textract.process(file_path).decode('utf-8')
|
48 |
+
inputs.append(text)
|
49 |
+
except Exception as e:
|
50 |
+
inputs.append(f"Error extracting text from {os.path.basename(file_path)}: {str(e)}")
|
51 |
+
return inputs
|
52 |
+
|
53 |
+
# Chat submit function
|
54 |
+
def chat_submit_func(message, files, chat_history, model, temperature, top_p, max_tokens, api_key):
|
55 |
+
client = genai.Client(api_key=api_key)
|
56 |
+
gen_model = client.models.get(model)
|
57 |
+
|
58 |
+
# Prepare inputs
|
59 |
+
if model_types[model] == "text" and files:
|
60 |
+
chat_history.append((message, "Warning: Files are not supported for text-only models. Converting to text where possible."))
|
61 |
+
processed_inputs = process_files(files, "text")
|
62 |
+
inputs = [message] + processed_inputs
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
63 |
else:
|
64 |
+
processed_inputs = process_files(files, model_types[model]) if files else []
|
65 |
+
inputs = [message] + processed_inputs
|
66 |
+
|
67 |
+
# Generation configuration
|
68 |
+
generation_config = {
|
69 |
+
"temperature": temperature,
|
70 |
+
"top_p": top_p,
|
71 |
+
"max_output_tokens": max_tokens,
|
72 |
+
}
|
73 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
74 |
try:
|
75 |
+
response = gen_model.generate_content(inputs, generation_config=generation_config)
|
76 |
+
response_text = ""
|
77 |
+
response_images = []
|
78 |
+
|
79 |
+
# Parse response
|
80 |
+
for candidate in response.candidates:
|
81 |
+
for part in candidate.content.parts:
|
82 |
+
if hasattr(part, 'text') and part.text:
|
83 |
+
response_text += part.text
|
84 |
+
elif hasattr(part, 'file_data') and part.file_data:
|
85 |
+
# Assuming file_data provides a URL; adjust if base64 or other format
|
86 |
+
image_url = part.file_data.url
|
87 |
+
response_images.append(image_url)
|
88 |
+
|
89 |
+
# Update chat history
|
90 |
+
user_message = message
|
91 |
+
if files:
|
92 |
+
user_message += "\nFiles: " + ", ".join([os.path.basename(f) for f in files])
|
93 |
+
chat_history.append((user_message, None))
|
94 |
+
|
95 |
+
bot_message = response_text
|
96 |
+
if response_images:
|
97 |
+
bot_message += "\n" + "\n".join([f"" for img in response_images])
|
98 |
+
chat_history.append((None, bot_message))
|
99 |
+
|
100 |
+
return chat_history, ""
|
101 |
except Exception as e:
|
102 |
+
chat_history.append((message, f"Error: {str(e)}"))
|
103 |
+
return chat_history, ""
|
104 |
+
|
105 |
+
# Single response submit function
|
106 |
+
def single_submit_func(prompt, files, model, temperature, top_p, max_tokens, api_key):
|
107 |
+
client = genai.Client(api_key=api_key)
|
108 |
+
gen_model = client.models.get(model)
|
109 |
+
|
110 |
+
# Prepare inputs
|
111 |
+
if model_types[model] == "text" and files:
|
112 |
+
processed_inputs = process_files(files, "text")
|
113 |
+
inputs = [prompt] + processed_inputs
|
114 |
+
warning = "Warning: Files converted to text for text-only model."
|
|
|
|
|
|
|
115 |
else:
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116 |
+
processed_inputs = process_files(files, model_types[model]) if files else []
|
117 |
+
inputs = [prompt] + processed_inputs
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118 |
+
warning = ""
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119 |
+
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120 |
+
# Generation configuration
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121 |
+
generation_config = {
|
122 |
+
"temperature": temperature,
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123 |
+
"top_p": top_p,
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124 |
+
"max_output_tokens": max_tokens,
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125 |
+
}
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126 |
+
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127 |
try:
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128 |
+
response = gen_model.generate_content(inputs, generation_config=generation_config)
|
129 |
+
response_text = warning
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130 |
+
response_images = []
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131 |
+
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132 |
+
# Parse response
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133 |
+
for candidate in response.candidates:
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134 |
+
for part in candidate.content.parts:
|
135 |
+
if hasattr(part, 'text') and part.text:
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136 |
+
response_text += part.text
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137 |
+
elif hasattr(part, 'file_data') and part.file_data:
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138 |
+
image_url = part.file_data.url
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139 |
+
response_images.append(image_url)
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140 |
+
|
141 |
+
return response_text, response_images
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|
142 |
except Exception as e:
|
143 |
+
return f"Error: {str(e)}", []
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|
144 |
|
145 |
+
# Gradio interface
|
146 |
+
with gr.Blocks(title="Gemini API Interface") as app:
|
147 |
# API Key Section
|
148 |
+
api_key_input = gr.Textbox(label="Gemini API Key", type="password", placeholder="Enter your Gemini API Key")
|
149 |
+
validate_btn = gr.Button("Validate API Key")
|
150 |
+
key_status = gr.Textbox(label="API Key Status", interactive=False)
|
151 |
+
key_validated = gr.State(False)
|
152 |
+
|
153 |
+
# Model and Parameters Section (hidden until key is validated)
|
154 |
+
with gr.Group(visible=False) as config_group:
|
155 |
+
model_selector = gr.Dropdown(choices=models, label="Select Model", value=models[0])
|
156 |
+
temperature = gr.Slider(0, 1, value=0.7, label="Temperature", step=0.01)
|
157 |
+
top_p = gr.Slider(0, 1, value=0.9, label="Top P", step=0.01)
|
158 |
+
max_tokens = gr.Number(value=512, label="Max Tokens", minimum=1)
|
159 |
+
|
160 |
+
# Tabs for Chat and Single Response (hidden until key is validated)
|
161 |
+
with gr.Tabs(visible=False) as tabs:
|
162 |
+
with gr.TabItem("Chat"):
|
163 |
+
chat_display = gr.Chatbot(label="Chat History")
|
164 |
+
chat_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
|
165 |
+
chat_files = gr.File(label="Upload Files", file_count="multiple")
|
166 |
+
chat_submit_btn = gr.Button("Send")
|
167 |
+
chat_status = gr.Textbox(label="Status", interactive=False)
|
168 |
+
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|
169 |
with gr.TabItem("Single Response"):
|
170 |
+
single_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
|
171 |
+
single_files = gr.File(label="Upload Files", file_count="multiple")
|
172 |
+
single_submit_btn = gr.Button("Generate")
|
173 |
+
single_text_output = gr.Textbox(label="Response Text", interactive=False)
|
174 |
+
single_image_output = gr.Gallery(label="Response Images")
|
175 |
+
|
176 |
+
# Validation logic
|
177 |
+
def on_validate_key(api_key):
|
178 |
+
is_valid, status = validate_api_key(api_key)
|
179 |
+
if is_valid:
|
180 |
+
return status, True, gr.update(visible=True), gr.update(visible=True)
|
181 |
+
return status, False, gr.update(visible=False), gr.update(visible=False)
|
182 |
+
|
183 |
+
validate_btn.click(
|
184 |
+
on_validate_key,
|
|
|
|
|
185 |
inputs=[api_key_input],
|
186 |
+
outputs=[key_status, key_validated, config_group, tabs]
|
|
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|
|
187 |
)
|
188 |
+
|
189 |
+
# Chat submission
|
190 |
+
chat_submit_btn.click(
|
191 |
+
chat_submit_func,
|
192 |
+
inputs=[chat_input, chat_files, chat_display, model_selector, temperature, top_p, max_tokens, api_key_input],
|
193 |
+
outputs=[chat_display, chat_status]
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
194 |
)
|
195 |
+
|
196 |
+
# Single response submission
|
197 |
+
single_submit_btn.click(
|
198 |
+
single_submit_func,
|
199 |
+
inputs=[single_input, single_files, model_selector, temperature, top_p, max_tokens, api_key_input],
|
200 |
+
outputs=[single_text_output, single_image_output]
|
|
|
|
|
|
|
|
|
|
|
201 |
)
|
202 |
|
203 |
+
app.launch()
|
|
|
|
|
|