Spaces:
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Running
poemsforaphrodite
commited on
Commit
•
05e591f
1
Parent(s):
80476ab
Update app.py
Browse files
app.py
CHANGED
@@ -14,7 +14,7 @@ from pinecone import Pinecone, ServerlessSpec
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import threading # {{ edit_25: Import threading for background processing }}
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import tiktoken
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from tiktoken.core import Encoding
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from runner import run_model
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from bson.objectid import ObjectId
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import traceback # Add this import at the top of your file
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import umap
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@@ -22,6 +22,39 @@ import plotly.graph_objs as go
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from sklearn.preprocessing import StandardScaler
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from sklearn.cluster import KMeans
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import plotly.colors as plc
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# Add this helper function at the beginning of your file
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def extract_prompt_text(prompt):
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@@ -81,8 +114,6 @@ def signup(username, password):
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"models": [] # List to store user's models
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})
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return True
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def upload_model(file):
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return "Model uploaded successfully!"
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# Function to perform evaluation (placeholder)
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def evaluate_model(model_identifier, metrics, username):
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@@ -151,10 +182,9 @@ def generate_embedding(text):
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try:
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embedding_response = openai_client.embeddings.create(
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model="text-embedding-3-large", # {{ edit_3: Use the specified embedding model }}
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input=text
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encoding_format="float"
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)
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embedding = embedding_response
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return embedding
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except Exception as e:
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st.error(f"Error generating embedding: {str(e)}")
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@@ -215,6 +245,7 @@ def index_context_data(model_name, texts):
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])
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except Exception as e:
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st.error(f"Error indexing data to Pinecone: {str(e)}")
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def upload_model(file, username, model_type):
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# {{ edit_5: Modify upload_model to handle model_type }}
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model_id = f"{username}_model_{int(datetime.now().timestamp())}"
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@@ -251,7 +282,56 @@ def upload_model(file, username, model_type):
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return f"Named Model {model_id} registered successfully!"
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else:
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return "Invalid model type specified."
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# Function to save results to MongoDB
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def save_results(username, model, prompt, context, response, evaluation): # {{ edit_29: Add 'username' parameter }}
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result = {
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@@ -267,6 +347,87 @@ def save_results(username, model, prompt, context, response, evaluation): # {{
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}
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results_collection.insert_one(result)
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# Modify the run_custom_evaluations function
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def run_custom_evaluations(data, selected_model, username):
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try:
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# For simple models, data is already in the correct format
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test_cases = data
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else:
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# For
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context_dataset, questions = data
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test_cases = [
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{
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"prompt": extract_prompt_text(question),
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"context":
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"response": "" # This will be filled by the model
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}
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for question in questions
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for test_case in test_cases:
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prompt_text = test_case["prompt"]
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context = test_case["context"]
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# Get the student model's response using runner.py
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try:
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-
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if answer is None or answer == "":
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st.warning(f"No response received from the model for prompt: {prompt_text}")
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answer = "No response received from the model."
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st.sidebar.error("Username already exists")
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else:
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st.sidebar.success(f"Welcome, {st.session_state.user}!")
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if st.sidebar.button("Logout"):
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st.session_state.user = None
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st.rerun()
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# App content
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if st.session_state.user:
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app_mode
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if app_mode == "Dashboard":
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st.title("Dashboard")
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st.write("### Real-time Metrics and Performance Insights")
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- **Model Performance**: Analyze clusters to identify strengths and weaknesses of models.
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- **Data Patterns**: Use clustering to uncover hidden patterns in your evaluation data.
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**Tips:**
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- Experiment with different numbers of clusters to find meaningful groupings.
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- Adjust UMAP parameters to see how the clustering changes with different embeddings.
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st.error(traceback.format_exc())
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st.stop()
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elif app_mode == "Model Upload":
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st.title("Upload Your Model")
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model_type = st.radio("Select Model Type", ["Custom", "Named"]) # {{ edit_6: Select model type }}
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uploaded_file = st.file_uploader("Choose a model file", type=[".pt", ".h5", ".bin"]) if model_type == "custom" else None
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else:
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st.error("Please upload a valid model file for Custom models.")
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elif app_mode == "Evaluation":
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st.title("Evaluate Your Model")
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st.write("### Select Model and Evaluation Metrics")
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else:
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st.error("Selected model not found.")
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elif app_mode == "Prompt Testing":
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st.title("Prompt Testing")
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if
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user_models = user.get("models", [])
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if not user_models:
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st.error("You have no uploaded models. Please upload a model first.")
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else:
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model_options = [
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f"{model['model_name']} ({model.get('model_type', 'Unknown').capitalize()})"
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for model in user_models
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]
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selected_model = st.selectbox("Select a Model for Testing", model_options)
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model_name = selected_model.split(" (")[0]
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model_type = selected_model.split(" (")[1].rstrip(")")
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else:
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# For simple models, we'll use a single JSON file
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if model_type.lower() == "simple":
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st.write("For simple models, please upload a single JSON file containing prompts, contexts, and responses.")
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json_file = st.file_uploader("Upload Test Data JSON", type=["json"])
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elif app_mode == "Manage Models":
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st.title("Manage Your Models")
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# Fetch the user from the database
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user = users_collection.find_one({"username": st.session_state.user})
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)
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st.subheader("Add a New Model")
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model_type = st.radio("Select Model Type:", ["Simple Model", "Custom Model"])
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if model_type == "Simple Model":
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new_model_name = st.text_input("Enter New Model Name:")
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if st.button("Add Simple Model")
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if new_model_name
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model_id = f"{st.session_state.user}_model_{int(datetime.now().timestamp())}"
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model_data = {
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"model_id": model_id,
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"model_name": new_model_name
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"model_type": "simple"
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"file_path": None,
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"model_link": None,
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"uploaded_at": datetime.now(),
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{"username": st.session_state.user},
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{"$push": {"models": model_data}}
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)
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st.success(f"Model '{
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else:
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st.error("Please enter a valid model name
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custom_model_options = ["gpt-4o", "gpt-4o-mini"]
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selected_custom_model = st.selectbox("Select Custom Model:", custom_model_options)
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}}}
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)
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st.success(f"Custom Model '{selected_custom_model}' added successfully as {model_id}!")
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st.markdown("---")
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{"$pull": {"models": {"model_id": model['model_id']}}}
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)
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st.success(f"Model {model['model_id']} deleted successfully!")
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else:
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st.info("You have no uploaded models.")
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elif app_mode == "History":
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st.title("History")
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st.write("### Your Evaluation History")
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st.info("You have no evaluation history yet.")
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except Exception as e:
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st.error(f"Error fetching history data: {e}")
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# Add a footer
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st.sidebar.markdown("---")
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st.sidebar.info("LLM Evaluation System - v0.2")
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import threading # {{ edit_25: Import threading for background processing }}
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import tiktoken
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from tiktoken.core import Encoding
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from runner import run_model, summarize_image # {{ edit_add: Import necessary functions }}
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from bson.objectid import ObjectId
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import traceback # Add this import at the top of your file
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import umap
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from sklearn.preprocessing import StandardScaler
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from sklearn.cluster import KMeans
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import plotly.colors as plc
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import uuid
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import time # Add this import at the top of your file
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from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration, AudioProcessorBase
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import av
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import io
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from typing import List
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import requests
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import traceback
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# Add these imports at the beginning of your file
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from pydub import AudioSegment
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# Add this import at the top of your file
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import tempfile
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# Add this helper function for audio recording
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def process_audio(frame):
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sound = frame.to_ndarray()
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sound = sound.astype(np.int16)
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return av.AudioFrame.from_ndarray(sound, layout="mono")
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# Add this helper function to convert WebRTC audio to a file
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def webrtc_audio_to_file(audio_frames):
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audio = AudioSegment.empty()
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for frame in audio_frames:
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audio += AudioSegment(
|
50 |
+
data=frame.to_ndarray().tobytes(),
|
51 |
+
sample_width=frame.format.bytes,
|
52 |
+
frame_rate=frame.sample_rate,
|
53 |
+
channels=1
|
54 |
+
)
|
55 |
+
buffer = io.BytesIO()
|
56 |
+
audio.export(buffer, format="wav")
|
57 |
+
return buffer.getvalue()
|
58 |
|
59 |
# Add this helper function at the beginning of your file
|
60 |
def extract_prompt_text(prompt):
|
|
|
114 |
"models": [] # List to store user's models
|
115 |
})
|
116 |
return True
|
|
|
|
|
117 |
|
118 |
# Function to perform evaluation (placeholder)
|
119 |
def evaluate_model(model_identifier, metrics, username):
|
|
|
182 |
try:
|
183 |
embedding_response = openai_client.embeddings.create(
|
184 |
model="text-embedding-3-large", # {{ edit_3: Use the specified embedding model }}
|
185 |
+
input=text
|
|
|
186 |
)
|
187 |
+
embedding = embedding_response.data[0].embedding
|
188 |
return embedding
|
189 |
except Exception as e:
|
190 |
st.error(f"Error generating embedding: {str(e)}")
|
|
|
245 |
])
|
246 |
except Exception as e:
|
247 |
st.error(f"Error indexing data to Pinecone: {str(e)}")
|
248 |
+
|
249 |
def upload_model(file, username, model_type):
|
250 |
# {{ edit_5: Modify upload_model to handle model_type }}
|
251 |
model_id = f"{username}_model_{int(datetime.now().timestamp())}"
|
|
|
282 |
return f"Named Model {model_id} registered successfully!"
|
283 |
else:
|
284 |
return "Invalid model type specified."
|
285 |
+
# {{ edit_30: Display uploaded models in the UI after uploading }}
|
286 |
+
st.write("### Uploaded Models")
|
287 |
+
user = users_collection.find_one({"username": username})
|
288 |
+
user_models = user.get("models", [])
|
289 |
+
for model in user_models:
|
290 |
+
st.write(f"- **{model['model_name']}** (ID: {model['model_id']})")
|
291 |
+
|
292 |
+
def run_huggingface_evaluations(data, selected_model, username):
|
293 |
+
try:
|
294 |
+
model_name = selected_model['model_name']
|
295 |
+
model_id = selected_model['model_id']
|
296 |
+
api_endpoint = selected_model.get('model_link')
|
297 |
+
api_token = selected_model.get('model_api_token')
|
298 |
+
|
299 |
+
if not api_endpoint or not api_token:
|
300 |
+
st.error("API endpoint or token is missing for the selected Hugging Face model.")
|
301 |
+
return
|
302 |
|
303 |
+
headers = {
|
304 |
+
"Authorization": f"Bearer {api_token}",
|
305 |
+
"Content-Type": "application/json"
|
306 |
+
}
|
307 |
+
|
308 |
+
for test_case in data:
|
309 |
+
prompt = test_case.get("prompt", "")
|
310 |
+
context = test_case.get("context", "")
|
311 |
+
|
312 |
+
# Prepare the payload for the Hugging Face API
|
313 |
+
payload = {
|
314 |
+
"inputs": f"Context: {context}\n\nPrompt: {prompt}"
|
315 |
+
}
|
316 |
+
|
317 |
+
# Make the API call to the Hugging Face model
|
318 |
+
response = requests.post(api_endpoint, headers=headers, json=payload)
|
319 |
+
|
320 |
+
if response.status_code == 200:
|
321 |
+
model_output = response.json()[0]['generated_text']
|
322 |
+
|
323 |
+
# Get the teacher's evaluation
|
324 |
+
evaluation = teacher_evaluate(prompt, context, model_output)
|
325 |
+
|
326 |
+
# Save the results
|
327 |
+
save_results(username, selected_model, prompt, context, model_output, evaluation)
|
328 |
+
else:
|
329 |
+
st.error(f"Error calling Hugging Face API: {response.status_code} - {response.text}")
|
330 |
+
|
331 |
+
st.success("Hugging Face model evaluation completed successfully!")
|
332 |
+
except Exception as e:
|
333 |
+
st.error(f"Error in Hugging Face evaluation: {str(e)}")
|
334 |
+
st.error(f"Detailed error: {traceback.format_exc()}")
|
335 |
# Function to save results to MongoDB
|
336 |
def save_results(username, model, prompt, context, response, evaluation): # {{ edit_29: Add 'username' parameter }}
|
337 |
result = {
|
|
|
347 |
}
|
348 |
results_collection.insert_one(result)
|
349 |
|
350 |
+
# Function to chunk text
|
351 |
+
def chunk_text(text, max_tokens=500):
|
352 |
+
tokens = tokenizer.encode(text)
|
353 |
+
chunks = []
|
354 |
+
current_chunk = []
|
355 |
+
current_length = 0
|
356 |
+
|
357 |
+
for token in tokens:
|
358 |
+
if current_length + 1 > max_tokens:
|
359 |
+
chunks.append(tokenizer.decode(current_chunk))
|
360 |
+
current_chunk = []
|
361 |
+
current_length = 0
|
362 |
+
current_chunk.append(token)
|
363 |
+
current_length += 1
|
364 |
+
|
365 |
+
if current_chunk:
|
366 |
+
chunks.append(tokenizer.decode(current_chunk))
|
367 |
+
|
368 |
+
return chunks
|
369 |
+
|
370 |
+
# Function to upload context to Pinecone
|
371 |
+
def upload_context_to_pinecone(context, username, model_name):
|
372 |
+
chunks = chunk_text(context)
|
373 |
+
index = pinecone_client.Index(os.getenv('PINECONE_INDEX_NAME'))
|
374 |
+
|
375 |
+
namespace = f"{username}_{model_name}" # Create a unique namespace for each user-model combination
|
376 |
+
|
377 |
+
for chunk in chunks:
|
378 |
+
embedding = generate_embedding(chunk)
|
379 |
+
if embedding:
|
380 |
+
index.upsert([
|
381 |
+
{
|
382 |
+
"id": str(uuid.uuid4()),
|
383 |
+
"values": embedding,
|
384 |
+
"metadata": {"text": chunk}
|
385 |
+
}
|
386 |
+
], namespace=namespace) # Use the namespace when upserting
|
387 |
+
|
388 |
+
# Function to retrieve relevant context from Pinecone
|
389 |
+
def retrieve_context_from_pinecone(prompt, username, model_name):
|
390 |
+
index = pinecone_client.Index(os.getenv('PINECONE_INDEX_NAME'))
|
391 |
+
prompt_embedding = generate_embedding(prompt)
|
392 |
+
|
393 |
+
namespace = f"{username}_{model_name}" # Use the same namespace format for retrieval
|
394 |
+
|
395 |
+
if prompt_embedding:
|
396 |
+
results = index.query(
|
397 |
+
vector=prompt_embedding,
|
398 |
+
top_k=5,
|
399 |
+
namespace=namespace, # Use the namespace when querying
|
400 |
+
include_metadata=True
|
401 |
+
)
|
402 |
+
|
403 |
+
retrieved_context = " ".join([result.metadata['text'] for result in results.matches])
|
404 |
+
return retrieved_context
|
405 |
+
|
406 |
+
return ""
|
407 |
+
|
408 |
+
def transcribe_audio(audio_file):
|
409 |
+
try:
|
410 |
+
# Save the uploaded file to a temporary file
|
411 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
|
412 |
+
temp_audio.write(audio_file.read())
|
413 |
+
temp_audio_path = temp_audio.name
|
414 |
+
|
415 |
+
# Transcribe the audio using OpenAI's Whisper model
|
416 |
+
with open(temp_audio_path, "rb") as audio_file:
|
417 |
+
transcript = openai_client.audio.transcriptions.create(
|
418 |
+
model="whisper-1",
|
419 |
+
file=audio_file,
|
420 |
+
response_format="text"
|
421 |
+
)
|
422 |
+
|
423 |
+
# Remove the temporary file
|
424 |
+
os.unlink(temp_audio_path)
|
425 |
+
|
426 |
+
return transcript
|
427 |
+
except Exception as e:
|
428 |
+
st.error(f"Error transcribing audio: {str(e)}")
|
429 |
+
return None
|
430 |
+
|
431 |
# Modify the run_custom_evaluations function
|
432 |
def run_custom_evaluations(data, selected_model, username):
|
433 |
try:
|
|
|
439 |
# For simple models, data is already in the correct format
|
440 |
test_cases = data
|
441 |
else:
|
442 |
+
# For custom models, data is split into context_dataset and questions
|
443 |
context_dataset, questions = data
|
444 |
+
|
445 |
+
# Upload context to Pinecone with user and model-specific namespace
|
446 |
+
upload_context_to_pinecone(context_dataset, username, model_name)
|
447 |
+
|
448 |
test_cases = [
|
449 |
{
|
450 |
"prompt": extract_prompt_text(question),
|
451 |
+
"context": "", # This will be filled with retrieved context
|
452 |
"response": "" # This will be filled by the model
|
453 |
}
|
454 |
for question in questions
|
|
|
456 |
|
457 |
for test_case in test_cases:
|
458 |
prompt_text = test_case["prompt"]
|
459 |
+
|
460 |
+
# For custom models, retrieve context from Pinecone using the user and model-specific namespace
|
461 |
+
if model_type != 'simple':
|
462 |
+
retrieved_context = retrieve_context_from_pinecone(prompt_text, username, model_name)
|
463 |
+
test_case["context"] = retrieved_context
|
464 |
+
|
465 |
context = test_case["context"]
|
466 |
|
467 |
# Get the student model's response using runner.py
|
468 |
try:
|
469 |
+
# Pass both prompt and context to run_model
|
470 |
+
answer = run_model(model_name, prompt_text, context)
|
471 |
if answer is None or answer == "":
|
472 |
st.warning(f"No response received from the model for prompt: {prompt_text}")
|
473 |
answer = "No response received from the model."
|
|
|
593 |
st.sidebar.error("Username already exists")
|
594 |
else:
|
595 |
st.sidebar.success(f"Welcome, {st.session_state.user}!")
|
596 |
+
|
597 |
+
# Separate links for each section
|
598 |
+
if st.sidebar.button("Dashboard"):
|
599 |
+
st.session_state.app_mode = "Dashboard"
|
600 |
+
st.rerun()
|
601 |
+
|
602 |
+
if st.sidebar.button("Model Upload"):
|
603 |
+
st.session_state.app_mode = "Model Upload"
|
604 |
+
st.rerun()
|
605 |
+
|
606 |
+
if st.sidebar.button("Evaluation"):
|
607 |
+
st.session_state.app_mode = "Evaluation"
|
608 |
+
st.rerun()
|
609 |
+
|
610 |
+
if st.sidebar.button("Prompt Testing"):
|
611 |
+
st.session_state.app_mode = "Prompt Testing"
|
612 |
+
st.rerun()
|
613 |
+
|
614 |
+
if st.sidebar.button("Manage Models"):
|
615 |
+
st.session_state.app_mode = "Manage Models"
|
616 |
+
st.rerun()
|
617 |
+
|
618 |
+
if st.sidebar.button("History"):
|
619 |
+
st.session_state.app_mode = "History"
|
620 |
+
st.rerun()
|
621 |
+
|
622 |
if st.sidebar.button("Logout"):
|
623 |
st.session_state.user = None
|
624 |
+
st.session_state.app_mode = None
|
625 |
st.rerun()
|
626 |
|
|
|
|
|
627 |
# App content
|
628 |
if st.session_state.user:
|
629 |
+
if 'app_mode' not in st.session_state:
|
630 |
+
st.session_state.app_mode = "Dashboard"
|
631 |
|
632 |
+
if st.session_state.app_mode == "Dashboard":
|
633 |
st.title("Dashboard")
|
634 |
st.write("### Real-time Metrics and Performance Insights")
|
635 |
|
|
|
1042 |
- **Model Performance**: Analyze clusters to identify strengths and weaknesses of models.
|
1043 |
- **Data Patterns**: Use clustering to uncover hidden patterns in your evaluation data.
|
1044 |
|
1045 |
+
**Tips:**
|
1046 |
|
1047 |
- Experiment with different numbers of clusters to find meaningful groupings.
|
1048 |
- Adjust UMAP parameters to see how the clustering changes with different embeddings.
|
|
|
1159 |
st.error(traceback.format_exc())
|
1160 |
st.stop()
|
1161 |
|
1162 |
+
elif st.session_state.app_mode == "Model Upload":
|
1163 |
st.title("Upload Your Model")
|
1164 |
model_type = st.radio("Select Model Type", ["Custom", "Named"]) # {{ edit_6: Select model type }}
|
1165 |
uploaded_file = st.file_uploader("Choose a model file", type=[".pt", ".h5", ".bin"]) if model_type == "custom" else None
|
|
|
1174 |
else:
|
1175 |
st.error("Please upload a valid model file for Custom models.")
|
1176 |
|
1177 |
+
elif st.session_state.app_mode == "Evaluation":
|
1178 |
st.title("Evaluate Your Model")
|
1179 |
st.write("### Select Model and Evaluation Metrics")
|
1180 |
|
|
|
1213 |
else:
|
1214 |
st.error("Selected model not found.")
|
1215 |
|
1216 |
+
elif st.session_state.app_mode == "Prompt Testing":
|
1217 |
st.title("Prompt Testing")
|
1218 |
|
1219 |
+
user = users_collection.find_one({"username": st.session_state.user})
|
1220 |
+
user_models = user.get("models", [])
|
1221 |
|
1222 |
+
if not user_models:
|
1223 |
+
st.error("You have no uploaded models. Please upload a model first.")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1224 |
else:
|
1225 |
+
model_options = [
|
1226 |
+
f"{model['model_name']} ({model.get('model_type', 'Unknown').capitalize()})"
|
1227 |
+
for model in user_models
|
1228 |
+
]
|
1229 |
+
selected_model = st.selectbox("Select a Model for Testing", model_options)
|
|
|
|
|
|
|
|
|
1230 |
|
1231 |
+
model_name = selected_model.split(" (")[0]
|
1232 |
+
model_type = selected_model.split(" (")[1].rstrip(")")
|
1233 |
+
|
1234 |
+
st.subheader("Input for Model Testing")
|
1235 |
+
|
1236 |
+
if model_type.lower() == "simple":
|
1237 |
+
input_type = st.radio("Select Input Type:", ["Text", "Audio", "Image"])
|
1238 |
+
elif model_type.lower() == "custom":
|
1239 |
+
input_type = "Text"
|
1240 |
+
elif model_type.lower() == "huggingface":
|
1241 |
+
input_type = "Text"
|
1242 |
+
|
1243 |
+
if input_type == "Text":
|
1244 |
+
if model_type.lower() == "simple":
|
1245 |
+
st.write("For simple models, please upload a single JSON file containing prompts, contexts, and responses.")
|
1246 |
+
json_file = st.file_uploader("Upload Test Data JSON", type=["json"])
|
1247 |
|
1248 |
+
if json_file is not None:
|
1249 |
+
try:
|
1250 |
+
test_data = json.load(json_file)
|
1251 |
+
st.success("Test data JSON file uploaded successfully!")
|
1252 |
+
|
1253 |
+
# Display a preview of the test data
|
1254 |
+
st.write("Preview of test data:")
|
1255 |
+
st.json(test_data[:3] if len(test_data) > 3 else test_data)
|
1256 |
+
|
1257 |
+
except json.JSONDecodeError:
|
1258 |
+
st.error("Invalid JSON format. Please check your file.")
|
1259 |
+
else:
|
1260 |
+
test_data = None
|
1261 |
+
elif model_type.lower() == "custom":
|
1262 |
+
# For other model types, keep the existing separate inputs for context and questions
|
1263 |
+
context_file = st.file_uploader("Upload Context Dataset", type=["txt"])
|
1264 |
+
if context_file is not None:
|
1265 |
+
context_dataset = context_file.getvalue().decode("utf-8")
|
1266 |
+
st.success("Context file uploaded successfully!")
|
1267 |
+
# Upload context to Pinecone with user and model-specific namespace
|
1268 |
+
upload_context_to_pinecone(context_dataset, st.session_state.user, model_name)
|
1269 |
+
else:
|
1270 |
+
context_dataset = None
|
1271 |
|
1272 |
+
questions_file = st.file_uploader("Upload Questions JSON", type=["json"])
|
1273 |
+
if questions_file is not None:
|
1274 |
+
questions_json = questions_file.getvalue().decode("utf-8")
|
1275 |
+
st.success("Questions file uploaded successfully!")
|
1276 |
+
else:
|
1277 |
+
questions_json = None
|
1278 |
+
elif model_type.lower() == "huggingface":
|
1279 |
+
st.write("For Hugging Face models, please enter your prompt:")
|
1280 |
+
context_file = st.file_uploader("Upload Context Dataset", type=["txt"])
|
1281 |
+
if context_file is not None:
|
1282 |
+
context_dataset = context_file.getvalue().decode("utf-8")
|
1283 |
+
st.success("Context file uploaded successfully!")
|
1284 |
+
else:
|
1285 |
+
context_dataset = None
|
1286 |
+
|
1287 |
+
questions_file = st.file_uploader("Upload Questions JSON", type=["json"])
|
1288 |
+
if questions_file is not None:
|
1289 |
+
questions_json = questions_file.getvalue().decode("utf-8")
|
1290 |
+
st.success("Questions file uploaded successfully!")
|
1291 |
+
else:
|
1292 |
+
questions_json = None
|
1293 |
+
|
1294 |
+
elif input_type == "Audio":
|
1295 |
+
st.write("Please upload audio files for Prompts, Contexts, and Responses.")
|
1296 |
+
prompt_audio = st.file_uploader("Upload Prompt Audio", type=["mp3", "wav"])
|
1297 |
+
context_audio = st.file_uploader("Upload Context Audio", type=["mp3", "wav"])
|
1298 |
+
response_audio = st.file_uploader("Upload Response Audio", type=["mp3", "wav"])
|
1299 |
+
|
1300 |
+
if prompt_audio:
|
1301 |
+
st.audio(prompt_audio, format='audio/wav')
|
1302 |
+
st.write(f"**Uploaded Prompt Audio:** {prompt_audio.name}")
|
1303 |
+
if context_audio:
|
1304 |
+
st.audio(context_audio, format='audio/wav')
|
1305 |
+
st.write(f"**Uploaded Context Audio:** {context_audio.name}")
|
1306 |
+
if response_audio:
|
1307 |
+
st.audio(response_audio, format='audio/wav')
|
1308 |
+
st.write(f"**Uploaded Response Audio:** {response_audio.name}")
|
1309 |
+
|
1310 |
+
elif input_type == "Image":
|
1311 |
+
st.write("Please upload image files for Prompt, Context, and Response.")
|
1312 |
+
prompt_image = st.file_uploader("Upload Prompt Image", type=["png", "jpg", "jpeg"])
|
1313 |
+
context_image = st.file_uploader("Upload Context Image", type=["png", "jpg", "jpeg"])
|
1314 |
+
response_image = st.file_uploader("Upload Response Image", type=["png", "jpg", "jpeg"])
|
1315 |
+
|
1316 |
+
if prompt_image:
|
1317 |
+
st.image(prompt_image, caption='Uploaded Prompt Image.', use_column_width=True)
|
1318 |
+
st.write(f"**Uploaded Prompt Image:** {prompt_image.name}")
|
1319 |
+
if context_image:
|
1320 |
+
st.image(context_image, caption='Uploaded Context Image.', use_column_width=True)
|
1321 |
+
st.write(f"**Uploaded Context Image:** {context_image.name}")
|
1322 |
+
if response_image:
|
1323 |
+
st.image(response_image, caption='Uploaded Response Image.', use_column_width=True)
|
1324 |
+
st.write(f"**Uploaded Response Image:** {response_image.name}")
|
1325 |
+
|
1326 |
+
# {{ edit_final: Handle Run Test for Image input with three images }}
|
1327 |
+
if st.button("Run Test"):
|
1328 |
+
if not model_name:
|
1329 |
+
st.error("Please select a valid Model.")
|
1330 |
+
elif input_type == "Text":
|
1331 |
+
if model_type.lower() == "simple" and test_data is None:
|
1332 |
+
st.error("Please upload a valid test data JSON file.")
|
1333 |
+
elif model_type.lower() != "simple" and (not context_dataset or not questions_json):
|
1334 |
+
st.error("Please provide both context dataset and questions JSON.")
|
1335 |
+
else:
|
1336 |
+
try:
|
1337 |
+
selected_model_data = next(
|
1338 |
+
(m for m in user_models if m['model_name'] == model_name),
|
1339 |
+
None
|
1340 |
+
)
|
1341 |
+
if selected_model_data:
|
1342 |
+
with st.spinner("Starting evaluations..."):
|
1343 |
+
if model_type.lower() == "simple":
|
1344 |
+
run_custom_evaluations(test_data, selected_model_data, st.session_state.user)
|
1345 |
+
st.success("Simple model evaluations are running in the background. You can navigate away or close the site.")
|
1346 |
+
elif model_type.lower() == "custom":
|
1347 |
+
questions = json.loads(questions_json)
|
1348 |
+
run_custom_evaluations((context_dataset, questions), selected_model_data, st.session_state.user)
|
1349 |
+
st.success("Custom model evaluations are running in the background. You can navigate away or close the site.")
|
1350 |
+
elif model_type.lower() == "huggingface":
|
1351 |
+
if not context_dataset or not questions_json:
|
1352 |
+
st.error("Please provide both context dataset and questions JSON.")
|
1353 |
+
else:
|
1354 |
+
try:
|
1355 |
+
questions = json.loads(questions_json)
|
1356 |
+
test_data = [
|
1357 |
+
{
|
1358 |
+
"prompt": extract_prompt_text(question),
|
1359 |
+
"context": context_dataset
|
1360 |
+
}
|
1361 |
+
for question in questions
|
1362 |
+
]
|
1363 |
+
run_huggingface_evaluations(test_data, selected_model_data, st.session_state.user)
|
1364 |
+
st.success("Hugging Face model evaluations are running in the background. You can navigate away or close the site.")
|
1365 |
+
except Exception as e:
|
1366 |
+
st.error(f"An error occurred: {str(e)}")
|
1367 |
+
st.error(f"Detailed error: {traceback.format_exc()}")
|
1368 |
else:
|
1369 |
+
st.error("Selected model not found.")
|
1370 |
+
except Exception as e:
|
1371 |
+
st.error(f"An error occurred: {str(e)}")
|
1372 |
+
st.error(f"Detailed error: {traceback.format_exc()}")
|
1373 |
+
st.success("Evaluations are running in the background. You can navigate away or close the site.")
|
1374 |
+
elif input_type == "Audio":
|
1375 |
+
if model_type.lower() == "simple" and test_data is None:
|
1376 |
+
st.error("Please upload a valid test data JSON file.")
|
1377 |
+
elif model_type.lower() != "simple" and (not context_dataset or not questions_json):
|
1378 |
+
st.error("Please provide both context dataset and questions JSON.")
|
1379 |
else:
|
1380 |
+
try:
|
1381 |
+
selected_model = next(
|
1382 |
+
(m for m in user_models if m['model_name'] == model_name),
|
1383 |
+
None
|
1384 |
+
)
|
1385 |
+
if selected_model:
|
1386 |
+
with st.spinner("Processing audio files..."):
|
1387 |
+
prompt_text = transcribe_audio(prompt_audio)
|
1388 |
+
context_text = transcribe_audio(context_audio)
|
1389 |
+
response_text = transcribe_audio(response_audio)
|
1390 |
+
|
1391 |
+
test_data = [
|
1392 |
+
{
|
1393 |
+
"prompt": prompt_text,
|
1394 |
+
"context": context_text,
|
1395 |
+
"response": response_text
|
1396 |
+
}
|
1397 |
+
]
|
1398 |
+
|
1399 |
+
with st.spinner("Starting evaluations..."):
|
1400 |
+
evaluation_thread = threading.Thread(
|
1401 |
+
target=run_custom_evaluations,
|
1402 |
+
args=(test_data, selected_model, st.session_state.user)
|
1403 |
+
)
|
1404 |
+
evaluation_thread.start()
|
1405 |
+
st.success("Evaluations are running in the background. You can navigate away or close the site.")
|
1406 |
+
else:
|
1407 |
+
st.error("Selected model not found.")
|
1408 |
+
except Exception as e:
|
1409 |
+
st.error(f"An error occurred: {e}")
|
1410 |
+
elif input_type == "Image":
|
1411 |
+
if not (prompt_image and context_image and response_image):
|
1412 |
+
st.error("Please upload all three image files: Prompt, Context, and Response.")
|
1413 |
+
else:
|
1414 |
+
try:
|
1415 |
+
selected_model = next(
|
1416 |
+
(m for m in user_models if m['model_name'] == model_name),
|
1417 |
+
None
|
1418 |
+
)
|
1419 |
+
if selected_model:
|
1420 |
+
with st.spinner("Processing images and starting evaluations..."):
|
1421 |
+
# Convert images to binary
|
1422 |
+
prompt_bytes = prompt_image.read()
|
1423 |
+
context_bytes = context_image.read()
|
1424 |
+
response_bytes = response_image.read()
|
1425 |
+
|
1426 |
+
# Use runner.py to summarize the images
|
1427 |
+
prompt_summary = summarize_image(prompt_bytes)
|
1428 |
+
context_summary = summarize_image(context_bytes)
|
1429 |
+
response_summary = summarize_image(response_bytes)
|
1430 |
+
|
1431 |
+
if prompt_summary and context_summary and response_summary:
|
1432 |
+
# Prepare test data with summaries
|
1433 |
+
test_data = [
|
1434 |
+
{
|
1435 |
+
"prompt": prompt_summary,
|
1436 |
+
"context": context_summary,
|
1437 |
+
"response": response_summary
|
1438 |
+
}
|
1439 |
+
]
|
1440 |
+
|
1441 |
+
# Start the evaluation in a separate thread
|
1442 |
+
evaluation_thread = threading.Thread(
|
1443 |
+
target=run_custom_evaluations,
|
1444 |
+
args=(test_data, selected_model, st.session_state.user)
|
1445 |
+
)
|
1446 |
+
evaluation_thread.start()
|
1447 |
+
st.success("Images processed and evaluations are running in the background. You can navigate away or close the site.")
|
1448 |
+
else:
|
1449 |
+
st.error("Failed to generate summaries for the uploaded images.")
|
1450 |
+
else:
|
1451 |
+
st.error("Selected model not found.")
|
1452 |
+
except Exception as e:
|
1453 |
+
st.error(f"An error occurred: {e}")
|
1454 |
+
elif input_type == "Image":
|
1455 |
+
if not (prompt_image and context_image and response_image):
|
1456 |
+
st.error("Please upload all three image files: Prompt, Context, and Response.")
|
1457 |
+
else:
|
1458 |
+
try:
|
1459 |
+
selected_model = next(
|
1460 |
+
(m for m in user_models if m['model_name'] == model_name),
|
1461 |
+
None
|
1462 |
+
)
|
1463 |
+
if selected_model:
|
1464 |
+
with st.spinner("Processing images and starting evaluations..."):
|
1465 |
+
# Convert images to binary
|
1466 |
+
prompt_bytes = prompt_image.read()
|
1467 |
+
context_bytes = context_image.read()
|
1468 |
+
response_bytes = response_image.read()
|
1469 |
+
|
1470 |
+
# Use runner.py to summarize the images
|
1471 |
+
prompt_summary = summarize_image(prompt_bytes)
|
1472 |
+
context_summary = summarize_image(context_bytes)
|
1473 |
+
response_summary = summarize_image(response_bytes)
|
1474 |
+
|
1475 |
+
if prompt_summary and context_summary and response_summary:
|
1476 |
+
# Prepare test data with summaries
|
1477 |
+
test_data = [
|
1478 |
+
{
|
1479 |
+
"prompt": prompt_summary,
|
1480 |
+
"context": context_summary,
|
1481 |
+
"response": response_summary
|
1482 |
+
}
|
1483 |
+
]
|
1484 |
+
|
1485 |
+
# Start the evaluation in a separate thread
|
1486 |
+
evaluation_thread = threading.Thread(
|
1487 |
+
target=run_custom_evaluations,
|
1488 |
+
args=(test_data, selected_model, st.session_state.user)
|
1489 |
+
)
|
1490 |
+
evaluation_thread.start()
|
1491 |
+
st.success("Images processed and evaluations are running in the background. You can navigate away or close the site.")
|
1492 |
+
else:
|
1493 |
+
st.error("Failed to generate summaries for the uploaded images.")
|
1494 |
+
else:
|
1495 |
+
st.error("Selected model not found.")
|
1496 |
+
except Exception as e:
|
1497 |
+
st.error(f"An error occurred: {e}")
|
1498 |
|
1499 |
+
elif st.session_state.app_mode == "Manage Models":
|
1500 |
st.title("Manage Your Models")
|
1501 |
# Fetch the user from the database
|
1502 |
user = users_collection.find_one({"username": st.session_state.user})
|
|
|
1515 |
)
|
1516 |
|
1517 |
st.subheader("Add a New Model")
|
1518 |
+
model_type = st.radio("Select Model Type:", ["Simple Model", "Custom Model","huggingface"])
|
1519 |
|
1520 |
if model_type == "Simple Model":
|
1521 |
new_model_name = st.text_input("Enter New Model Name:")
|
1522 |
+
if st.button("Add Simple Model"):
|
1523 |
+
if new_model_name:
|
1524 |
model_id = f"{st.session_state.user}_model_{int(datetime.now().timestamp())}"
|
1525 |
model_data = {
|
1526 |
"model_id": model_id,
|
1527 |
+
"model_name": new_model_name,
|
1528 |
+
"model_type": "simple",
|
1529 |
"file_path": None,
|
1530 |
"model_link": None,
|
1531 |
"uploaded_at": datetime.now(),
|
|
|
1535 |
{"username": st.session_state.user},
|
1536 |
{"$push": {"models": model_data}}
|
1537 |
)
|
1538 |
+
st.success(f"Model '{new_model_name}' added successfully as {model_id}!")
|
1539 |
else:
|
1540 |
+
st.error("Please enter a valid model name.")
|
1541 |
|
1542 |
+
elif model_type == "Custom Model": # Custom Model
|
1543 |
custom_model_options = ["gpt-4o", "gpt-4o-mini"]
|
1544 |
selected_custom_model = st.selectbox("Select Custom Model:", custom_model_options)
|
1545 |
|
|
|
1557 |
}}}
|
1558 |
)
|
1559 |
st.success(f"Custom Model '{selected_custom_model}' added successfully as {model_id}!")
|
1560 |
+
else:
|
1561 |
+
model_name = st.text_input("Enter Hugging Face Model Name:")
|
1562 |
+
api_endpoint = st.text_input("Enter Hugging Face API Endpoint:")
|
1563 |
+
api_token = st.text_input("Enter Hugging Face API Token:", type="password")
|
1564 |
+
|
1565 |
+
if st.button("Add Hugging Face Model"):
|
1566 |
+
if api_endpoint and api_token:
|
1567 |
+
model_id = f"{st.session_state.user}_model_{int(datetime.now().timestamp())}"
|
1568 |
+
model_data = {
|
1569 |
+
"model_id": model_id,
|
1570 |
+
"model_name": model_name,
|
1571 |
+
"model_type": "huggingface",
|
1572 |
+
"file_path": None,
|
1573 |
+
"model_link": api_endpoint,
|
1574 |
+
"model_api_token": api_token,
|
1575 |
+
"uploaded_at": datetime.now()
|
1576 |
+
}
|
1577 |
+
users_collection.update_one(
|
1578 |
+
{"username": st.session_state.user},
|
1579 |
+
{"$push": {"models": model_data}}
|
1580 |
+
)
|
1581 |
+
st.success(f"Hugging Face Model '{model_name}' added successfully as {model_id}!")
|
1582 |
|
1583 |
st.markdown("---")
|
1584 |
|
|
|
1604 |
{"$pull": {"models": {"model_id": model['model_id']}}}
|
1605 |
)
|
1606 |
st.success(f"Model {model['model_id']} deleted successfully!")
|
1607 |
+
st.rerun()
|
1608 |
else:
|
1609 |
st.info("You have no uploaded models.")
|
1610 |
|
1611 |
+
elif st.session_state.app_mode == "History":
|
1612 |
st.title("History")
|
1613 |
st.write("### Your Evaluation History")
|
1614 |
|
|
|
1688 |
st.info("You have no evaluation history yet.")
|
1689 |
|
1690 |
except Exception as e:
|
1691 |
+
st.error(f"Error fetching history data: {e}")
|
|
|
|
|
|
|
|