Delete app.py.old2

app.py.old2

@@ -1,225 +0,0 @@
-import os
-import gradio as gr
-import requests
-import json
-import logging
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-
-# API Keys configuration
-COHERE_API_KEY = os.getenv("COHERE_API_KEY")
-MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-
-if not all([COHERE_API_KEY, MISTRAL_API_KEY, GEMINI_API_KEY]):
-    raise ValueError("Missing required API keys in environment variables")
-
-# API endpoints configuration
-COHERE_API_URL = "https://api.cohere.ai/v1/chat"
-MISTRAL_API_URL = "https://api.mistral.ai/v1/chat/completions"
-GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-002:generateContent"
-VECTOR_API_URL = "https://sendthat.cc"
-HISTORY_INDEX = "onramps"
-
-# Model configurations
-MODELS = {
-    "Cohere": {
-        "name": "command-r-08-2024",
-        "api_url": COHERE_API_URL,
-        "api_key": COHERE_API_KEY
-    },
-    "Mistral": {
-        "name": "ft:open-mistral-nemo:ef730d29:20241022:2a0e7d46",
-        "api_url": MISTRAL_API_URL,
-        "api_key": MISTRAL_API_KEY
-    },
-    "Gemini": {
-        "name": "gemini-1.5-pro-002",
-        "api_url": GEMINI_API_URL,
-        "api_key": GEMINI_API_KEY
-    }
-}
-
-def search_document(query, k):
-    try:
-        url = f"{VECTOR_API_URL}/search/{HISTORY_INDEX}"
-        payload = {"text": query, "k": k}
-        headers = {"Content-Type": "application/json"}
-        response = requests.post(url, json=payload, headers=headers)
-        response.raise_for_status()
-        return response.json(), "", k
-    except requests.exceptions.RequestException as e:
-        logging.error(f"Error in search: {e}")
-        return {"error": str(e)}, query, k
-
-def generate_answer_cohere(question, context, citations):
-    headers = {
-        "Authorization": f"Bearer {MODELS['Cohere']['api_key']}",
-        "Content-Type": "application/json"
-    }
-    
-    prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer the question based on the given context. Include citations as [1], [2], etc.:"
-    
-    payload = {
-        "message": prompt,
-        "model": MODELS['Cohere']['name'],
-        "preamble": "You are an AI-assistant chatbot. Provide thorough responses with citations.",
-        "chat_history": []
-    }
-    
-    try:
-        response = requests.post(MODELS['Cohere']['api_url'], headers=headers, json=payload)
-        response.raise_for_status()
-        answer = response.json()['text']
-        
-        answer += "\n\nSources:"
-        for i, citation in enumerate(citations, 1):
-            answer += f"\n[{i}] {citation}"
-        
-        return answer
-    except requests.exceptions.RequestException as e:
-        logging.error(f"Error in generate_answer_cohere: {e}")
-        return f"An error occurred: {str(e)}"
-
-def generate_answer_mistral(question, context, citations):
-    headers = {
-        "Authorization": f"Bearer {MODELS['Mistral']['api_key']}",
-        "Content-Type": "application/json",
-        "Accept": "application/json"
-    }
-    
-    prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer the question based on the given context. Include citations as [1], [2], etc.:"
-    
-    payload = {
-        "model": MODELS['Mistral']['name'],
-        "messages": [
-            {
-                "role": "user",
-                "content": prompt
-            }
-        ]
-    }
-    
-    try:
-        response = requests.post(MODELS['Mistral']['api_url'], headers=headers, json=payload)
-        response.raise_for_status()
-        answer = response.json()['choices'][0]['message']['content']
-        
-        answer += "\n\nSources:"
-        for i, citation in enumerate(citations, 1):
-            answer += f"\n[{i}] {citation}"
-        
-        return answer
-    except requests.exceptions.RequestException as e:
-        logging.error(f"Error in generate_answer_mistral: {e}")
-        return f"An error occurred: {str(e)}"
-
-def generate_answer_gemini(question, context, citations):
-    headers = {
-        "Content-Type": "application/json"
-    }
-    
-    prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer the question based on the given context. Include citations as [1], [2], etc.:"
-    
-    payload = {
-        "contents": [
-            {
-                "role": "user",
-                "parts": [
-                    {
-                        "text": prompt
-                    }
-                ]
-            }
-        ],
-        "generationConfig": {
-            "temperature": 1,
-            "topK": 40,
-            "topP": 0.95,
-            "maxOutputTokens": 8192,
-            "responseMimeType": "text/plain"
-        }
-    }
-    
-    try:
-        url = f"{MODELS['Gemini']['api_url']}?key={MODELS['Gemini']['api_key']}"
-        response = requests.post(url, headers=headers, json=payload)
-        response.raise_for_status()
-        answer = response.json()['candidates'][0]['content']['parts'][0]['text']
-        
-        answer += "\n\nSources:"
-        for i, citation in enumerate(citations, 1):
-            answer += f"\n[{i}] {citation}"
-        
-        return answer
-    except requests.exceptions.RequestException as e:
-        logging.error(f"Error in generate_answer_gemini: {e}")
-        return f"An error occurred: {str(e)}"
-
-def answer_question(question, model_choice, k=3):
-    # Search the vector database
-    search_results, _, _ = search_document(question, k)
-    
-    # Extract and combine the retrieved contexts
-    if "results" in search_results:
-        contexts = []
-        citations = []
-        for item in search_results['results']:
-            contexts.append(item['metadata']['content'])
-            citations.append(f"{item['metadata'].get('title', 'Unknown Source')} - {item['metadata'].get('source', 'No source provided')}")
-        combined_context = " ".join(contexts)
-    else:
-        logging.error(f"Error in database search or no results found: {search_results}")
-        combined_context = ""
-        citations = []
-    
-    # Generate answer using the selected model
-    if model_choice == "Cohere":
-        return generate_answer_cohere(question, combined_context, citations)
-    elif model_choice == "Mistral":
-        return generate_answer_mistral(question, combined_context, citations)
-    else:
-        return generate_answer_gemini(question, combined_context, citations)
-
-def chatbot(message, history, model_choice):
-    response = answer_question(message, model_choice)
-    return response
-
-# Example questions with default model choice
-EXAMPLE_QUESTIONS = [
-    ["Why was Anne Hutchinson banished from Massachusetts?", "Cohere"],
-    ["What were the major causes of World War I?", "Mistral"],
-    ["Who was the first President of the United States?", "Gemini"],
-    ["What was the significance of the Industrial Revolution?", "Cohere"]
-]
-
-# Create Gradio interface
-with gr.Blocks(theme="soft") as iface:
-    gr.Markdown("# History Chatbot")
-    gr.Markdown("Ask me anything about history, and I'll provide answers with citations!")
-    
-    with gr.Row():
-        model_choice = gr.Radio(
-            choices=["Cohere", "Mistral", "Gemini"],
-            value="Cohere",
-            label="Choose LLM Model",
-            info="Select which AI model to use for generating responses"
-        )
-    
-    chatbot_interface = gr.ChatInterface(
-        fn=lambda message, history, model: chatbot(message, history, model),
-        additional_inputs=[model_choice],
-        chatbot=gr.Chatbot(height=300),
-        textbox=gr.Textbox(placeholder="Ask a question about history...", container=False, scale=7),
-        examples=EXAMPLE_QUESTIONS,
-        cache_examples=False,
-        retry_btn=None,
-        undo_btn="Delete Previous",
-        clear_btn="Clear",
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()