Create app.py

app.py

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+# Install required libraries
+!pip install fastapi uvicorn pyngrok nest-asyncio langchain langchain-community langchain-openai
+
+# Import necessary libraries
+from fastapi import FastAPI
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel
+import uvicorn
+import nest_asyncio
+from pyngrok import ngrok, conf
+
+# Apply asyncio patch to run FastAPI inside Colab
+nest_asyncio.apply()
+
+# Initialize FastAPI application
+app = FastAPI()
+
+# Initialize OpenAI language model (Set your API key)
+llm = ChatOpenAI(model_name="gpt-4", openai_api_key="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")  # Replace with your actual API key
+
+# Define a request model for structured API input
+class QueryRequest(BaseModel):
+    prompt: str
+
+# Define an endpoint to handle AI queries
+@app.post("/query")
+def query_llm(request: QueryRequest):
+    """
+    Handles user queries and returns AI-generated responses.
+
+    Parameters:
+        request (QueryRequest): JSON payload containing the prompt.
+
+    Returns:
+        dict: AI response as JSON.
+    """
+    response = llm.predict(request.prompt)  # Call OpenAI model to generate response
+    return {"response": response}  # Return response as JSON
+
+# Set up ngrok to expose the FastAPI app
+# Set your authtoken here
+conf.get_default().auth_token = "2tTyxCxX3Ckk5HKetSNlNBqTepQ_6mss4xgMTbfUC5E6esgvU"  # Replace with your actual authtoken
+public_url = ngrok.connect(8000).public_url
+print(f"Public API URL: {public_url}")
+
+# Run FastAPI using Uvicorn inside Colab
+uvicorn.run(app, host="0.0.0.0", port=8000)