Delete app.py

app.py

@@ -1,176 +0,0 @@
-
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-import chromadb
-import pandas as pd
-import os
-import json
-from pathlib import Path
-
-# Load the sentence transformer model
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Initialize the ChromaDB client
-chroma_client = chromadb.Client()
-
-# Function to build the database from CSV
-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
-    # chroma_client.delete_collection(name=collection_name)
-    
-    # Create a new collection
-    collection = chroma_client.create_collection(name=collection_name)
-    
-    # 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(lambda x: eval(x.replace(',,', ','))).tolist()
-    )
-    
-    return collection
-
-# Build the database when the app starts
-collection = build_database()
-
-# Instantiate the Anyscale client
-client = Anyscale(api_key=anyscale_api_key, model="meta-llama/Llama-2-70b-chat-hf")
-
-# Function to get 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
-
-# Define system message for LLM
-qna_system_message = """
-You are an assistant to Finsights analysts. Your task is to provide relevant information about the financial performance of the companies followed by Finsights.
-
-User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
-The context contains references to specific portions of documents relevant to the user's query, along with source links.
-The source for a context will begin with the token: ###Source.
-
-When crafting your response:
-1. Select only the context relevant to answer the question.
-2. Include the source links in your response.
-3. User questions will begin with the token: ###Question.
-4. If the question is irrelevant to Finsights, respond with: "I am an assistant for Finsight Docs. I can only help you with questions related to Finsights."
-
-Adhere to the following guidelines:
-- Your response should only address the question asked and nothing else.
-- Answer only using the context provided.
-- Do not mention anything about the context in your final answer.
-- If the answer is not found in the context, respond with: "I don't know."
-- Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
-- Do not make up sources. Use only the links provided in the sources section of the context. You are prohibited from providing other links/sources.
-
-Here is an example of how to structure your response:
-
-Answer:
-[Answer]
-
-Source:
-[Source]
-"""
-# Create a user message template
-qna_user_message_template = """
-###Context
-Here are some documents and their source links that are relevant to the question mentioned below.
-{context}
-
-###Question
-{question}
-"""
-
-# 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)  # Increase max_tokens if possible
-            chunk = response.text.strip()
-            full_response += chunk
-            if chunk.endswith((".", "!", "?")):  # Check if response seems complete
-                break
-            else:
-                prompt = "Please continue from where you left off:\n" + chunk[-100:]  # Use the last 100 chars as context
-        except Exception as e:
-            print(f"Attempt {attempt + 1} failed with error: {e}")
-    return full_response
-
-# Prediction function
-def predict(company, user_query):
-    try:
-        # 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"
-
-        # Prepare the user message
-        user_message = qna_user_message_template.format(context=context, question=user_query)
-
-        # Craft the prompt to pass to the Llama model
-        prompt = f"{qna_system_message}\n\n{qna_user_message_template.format(context=context, question=user_query)}"
-
-        # Generate the response using the Llama model through Anyscale
-        answer = get_llm_response(prompt)
-
-        # Extract the generated response
-        # answer = response.text.strip()
-        
-        # Log the interaction
-        log_interaction(company, user_query, context, answer)
-        
-        return answer
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# Function to log interactions
-def log_interaction(company, user_query, context, answer):
-    log_file = Path("interaction_log.jsonl")
-    with log_file.open("a") as f:
-        json.dump({
-            'company': company,
-            'user_query': user_query,
-            'context': context,
-            'answer': answer
-        }, f)
-        f.write("\n")
-
-# 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()