Delete app.py

app.py

@@ -1,130 +0,0 @@
-
-# Import the necessary Libraries
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-import chromadb
-import pandas as pd
-import os
-
-# Load the sentence transformer model
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Initialize the ChromaDB client
-client = chromadb.Client()
-
-def build_database():
-    # Read the CSV file
-    df = pd.read_csv('collection_data.csv')
-    
-    # Create a collection
-    collection_name = 'Dataset-10k-companies'
-    
-    # Delete the existing collection if it exists
-    if collection_name in client.list_collections():
-        client.delete_collection(name=collection_name)
-    
-    # Create a new collection
-    collection = client.create_collection(name=collection_name)
-    
-    # Function to safely process embeddings
-    def process_embedding(x):
-        if isinstance(x, str):
-            return eval(x.replace(',,', ','))
-        elif isinstance(x, float):
-            return []  # or some default value
-        else:
-            return x
-
-    # Add the data from the DataFrame to the collection
-    collection.add(
-        documents=df['documents'].tolist(),
-        ids=df['ids'].tolist(),
-        metadatas=df['metadatas'].apply(eval).tolist(),
-        embeddings=df['embeddings'].apply(process_embedding).tolist()
-    )
-    
-    return collection
-
-# Build the database when the app starts
-collection = build_database()
-
-# Function to perform similarity search and return relevant chunks
-def get_relevant_chunks(query, collection, top_n=3):
-    query_embedding = model.encode(query).tolist()
-    results = collection.query(query_embeddings=[query_embedding], n_results=top_n)
-    
-    relevant_chunks = []
-    for i in range(len(results['documents'][0])):
-        chunk = results['documents'][0][i]
-        source = results['metadatas'][0][i]['source']
-        page = results['metadatas'][0][i]['page']
-        relevant_chunks.append((chunk, source, page))
-    
-    return relevant_chunks
-
-# Function to get LLM response
-def get_llm_response(prompt, max_attempts=3):
-    full_response = ""
-    for attempt in range(max_attempts):
-        try:
-            response = client.complete(prompt, max_tokens=1000)
-            chunk = response.text.strip()
-            full_response += chunk
-            if chunk.endswith((".", "!", "?")):
-                break
-            else:
-                prompt = "Please continue from where you left off:\n" + chunk[-100:]
-        except Exception as e:
-            print(f"Attempt {attempt + 1} failed with error: {e}")
-    return full_response
-
-# Prediction function
-def predict(company, user_query):
-    # Modify the query to include the company name
-    modified_query = f"{user_query} for {company}"
-    
-    # Get relevant chunks
-    relevant_chunks = get_relevant_chunks(modified_query, collection)
-    
-    # Prepare the context string
-    context = ""
-    for chunk, source, page in relevant_chunks:
-        context += chunk + "\n"
-        context += f"[Source: {source}, Page: {page}]\n\n"
-    
-    # Generate answer
-    prompt = f"Based on the following context, answer the question: {modified_query}\n\nContext:\n{context}"
-    prediction = get_llm_response(prompt)
-    
-    # While the prediction is made, log both the inputs and outputs to a local log file
-    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
-    # access
-
-    with scheduler.lock:
-        with log_file.open("a") as f:
-            f.write(json.dumps(
-                {
-                    'user_input': user_input,
-                    'retrieved_context': context_for_query,
-                    'model_response': prediction
-                }
-            ))
-            f.write("\n")
-
-    return prediction
-
-# Create Gradio interface
-company_list = ["MSFT", "AWS", "Meta", "Google", "IBM"]
-iface = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Radio(company_list, label="Select Company"),
-        gr.Textbox(lines=2, placeholder="Enter your query here...", label="User Query")
-    ],
-    outputs=gr.Textbox(label="Generated Answer"),
-    title="Company Reports Q&A",
-    description="Query the vector database and get an LLM response based on the documents in the collection."
-)
-
-# Launch the interface
-iface.launch()