Update app.py

app.py

@@ -1,94 +1,13 @@
 import uvicorn
 import threading
-import os
-from fastapi import FastAPI
-from pydantic import BaseModel
-from groq import Groq
-import gradio as gr
-import requests
+import chat_fast_api
+import gradio_app
 
-# Initialize the FastAPI app
-app = FastAPI()
 
-# Get the API key from an environment variable
-groq_api_key = os.getenv("GROQ_API_KEY")
-if not groq_api_key:
-    raise ValueError("GROQ_API_KEY environment variable is not set")
+def start_fast():
+    uvicorn.run("chat_fast_api:app",host="0.0.0.0",port=8000)
 
-# Initialize Groq client
-client = Groq(api_key=groq_api_key)
+fastapi_thread = threading.Thread(target=start_fast)
+fastapi_thread.start()
 
-# Define the system message
-System_msg = '''-act as an experienced blockchain developer, working for 15 years.
-                -help me understand some concepts, assume I am a complete beginner.
-                -If the user asks anything not related to blockchain, just say you don't know about it.'''
-
-# Request model for FastAPI
-class ChatRequest(BaseModel):
-    message: str
-    history: list
-
-# FastAPI chat endpoint
-@app.post("/chat")
-def chat(request: ChatRequest):
-    message = request.message
-    history = request.history
-
-    # Create the history_list to send to the Groq API
-    history_list = [{"role": "system", "content": System_msg}]
-    for human, ai in history:
-        history_list.append({"role": "user", "content": human})
-        history_list.append({"role": "assistant", "content": ai})
-
-    # Append the new user message to the history
-    history_list.append({"role": "user", "content": message})
-
-    # Try to get the response from the LLaMA API (Groq)
-    try:
-        response = client.chat.completions.create(
-            model="llama-3.1-70b-versatile",  # Ensure the correct model name
-            messages=history_list,
-            temperature=1.0,
-            max_tokens=4000,
-            stream=False  # Use streaming for real-time responses
-        )
-
-        final_message = response.choices[0].message.content
-
-        # Return the final AI-generated message
-        return {"response": final_message}
-
-    except Exception as e:
-        return {"response": f"Error: {str(e)}"}
-
-# Gradio Interface
-def predict(message, history):
-    # API request to FastAPI backend
-    response = requests.post("http://0.0.0.0:8000/chat", json={"message": message, "history": history})
-    
-    if response.status_code == 200:
-        response_data = response.json()
-        return response_data['response']
-    else:
-        return "Error: Unable to connect to the FastAPI backend."
-
-demo = gr.ChatInterface(
-    predict,
-    title="Blockchain Teacher",
-    theme=gr.themes.Soft(),
-    chatbot=gr.Chatbot(label="Learn about blockchain technology"),
-    textbox=gr.Textbox(
-        placeholder="Ask me anything about blockchain",
-        scale=7,
-        max_lines=2,
-    ),
-)
-
-# Function to run FastAPI in a separate thread
-def start_fastapi():
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-
-# Start FastAPI and Gradio
-if __name__ == "__main__":
-    threading.Thread(target=start_fastapi).start()
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+gradio_app.start_gradio()