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app.py

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+# Requirement: !pip install gradio, groq
+
+# 1. Imports and API setup
+import gradio as gr
+from groq import Groq
+import base64
+import os
+import spaces
+
+# Image encoding function
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Image to text function
+def image_to_text(client, model, base64_image, prompt):
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{base64_image}",
+                            },
+                        },
+                    ],
+                }
+            ],
+            model=model
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        if 'Invalid API Key' in str(e):
+            return "Please enter a correct API key and try again."
+        return f"Error generating text from image: {str(e)}"
+
+# Technical review generation function
+def technical_review_generation(client, image_description):
+    keywords = ["econometrics", "finance", "marketing", "stock", "prediction", "chart", "graph", "time series"]
+    if not any(keyword in image_description.lower() for keyword in keywords):
+        return "The image is not related to the area this app covers. Please input a relevant image."
+
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a professional econometrics. Write a complete review and report about the scene depicted in this image.",
+                },
+                {
+                    "role": "user",
+                    "content": image_description,
+                }
+            ],
+            model=llama31_model
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        return f"Error generating report: {str(e)}"
+
+# Main function for Gradio interface
+def process_image(api_key, image, prompt="Describe this image in detail."):
+    # Set the API key
+    try:
+        os.environ["GROQ_API_KEY"] = api_key
+        client = Groq()  # Initialize the Groq client with the provided key
+    except Exception as e:
+        return "Please enter a correct API key and try again.", ""
+
+    # Encode the image
+    base64_image = encode_image(image)
+
+    # Get image description from the model
+    image_description = image_to_text(client, llava_model, base64_image, prompt)
+
+    # If API key was invalid, only return the API key error message
+    if "Please enter a correct API key and try again." in image_description:
+        return image_description, ""
+
+    # Generate the econometrics report based on the image description
+    report = technical_review_generation(client, image_description)
+
+    # Return both image description and the econometrics report
+    return f"--- Image Description ---\n{image_description}", f"--- GroqLLaVA EconoMind Report ---\n{report}"
+
+# Define CSS for centering elements and footer styling
+css = """
+    #title, #description {
+        text-align: center;
+        margin: 20px;
+    }
+    #footer {
+        text-align: center;
+        margin-top: 30px;
+        padding: 10px;
+        font-size: 14px;
+    }
+    .gradio-container {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+    }
+    .gradio-row {
+        width: 100%;
+        display: flex;
+        justify-content: center;
+    }
+    .clear-button {
+        margin-top: 10px;
+    }
+"""
+
+# Gradio Interface
+@spaces.GPU()
+def gradio_interface():
+    # Define the footer HTML
+    footer = """
+    <div id="footer">
+        <a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
+        <a href="https://github.com/arad1367" target="_blank">GitHub</a> |
+        <a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> |
+        <a href="https://groq.com/introducing-llava-v1-5-7b-on-groqcloud-unlocking-the-power-of-multimodal-ai/" target="_blank">Introducing LLaVA V1.5 7B on GroqCloud</a>
+        <br>
+        Made with 💖 by Pejman Ebrahimi
+    </div>
+    """
+
+    with gr.Blocks(theme="gradio/soft", css=css) as demo:
+        gr.HTML("<h1 id='title'>GroqLLaVA Econometrics Agent</h1>")
+        gr.HTML("<p id='description'>Upload an economic chart and get a detailed analysis using Groq + LLaVA V1.5 7B multimodal.</p>")
+        
+        with gr.Row():
+            api_key_input = gr.Textbox(label="GROQ API Key", placeholder="Enter your GROQ API Key", type="password")
+        with gr.Row():
+            image_input = gr.Image(type="filepath", label="Upload an Image")  # Changed type to 'filepath'
+        with gr.Row():
+            report_button = gr.Button("Generate Report")
+        with gr.Row():
+            output_description = gr.Textbox(label="Image Description", lines=10, elem_id="description-box")
+            output_report = gr.Textbox(label="Report", lines=10, elem_id="report-box")
+
+        # Define the interaction between inputs and outputs
+        report_button.click(
+            fn=process_image,
+            inputs=[api_key_input, image_input],
+            outputs=[output_description, output_report]
+        )
+
+        # Add footer HTML
+        gr.HTML(footer)
+        
+        # Add clear button
+        def clear_inputs():
+            return "", None, "", ""
+
+        with gr.Row():
+            clear_button = gr.Button("Clear", elem_id="clear-button")
+            clear_button.click(
+                fn=clear_inputs,
+                inputs=[],
+                outputs=[api_key_input, image_input, output_description, output_report]
+            )
+
+    # Launch the interface
+    demo.launch()
+
+# Define models used in the process
+llava_model = 'llava-v1.5-7b-4096-preview'
+llama31_model = 'llama-3.1-70b-versatile'
+
+# Start the Gradio interface
+gradio_interface()

requirements.txt

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+groq
+gradio