added files

app.py

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+import uvicorn
+import threading
+import chat_fast_api
+import gradio_app
+
+
+def start_fast():
+    uvicorn.run("chat_fast_api:app",host="0.0.0.0",port=8000)
+
+fastapi_thread = threading.Thread(target=start_fast)
+fastapi_thread.start()
+
+gradio_app.start_gradio()

chat_fast_api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from groq import Groq
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+# 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")
+
+# Initialize Groq client
+client = Groq(api_key=groq_api_key)
+
+# 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_app.py

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+import gradio as gr
+import requests
+
+def predict(message, history):
+    # API request to FastAPI backend
+    response = requests.post("http://127.0.0.1:8000/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,
+    ),
+)
+
+def start_gradio():
+    demo.launch(server_name="0.0.0.0", server_port= 7860)