Create main.py

main.py

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+from fastapi import FastAPI, Request
+import openai
+from pydantic import BaseModel
+import os
+
+# Define a Pydantic model for the input
+class RoadmapLogs(BaseModel):
+    roadmap_logs: str
+
+# Initialize the FastAPI app
+app = FastAPI()
+
+# Define OpenAI API key (replace this with your method to fetch the key, such as an environment variable)
+openai.api_key = os.getenv('RaghavOPENAIKey')
+
+# Define the prompt and system role
+role = """You are an expert assistant who analyzes student performance on their study roadmap and provides insights based on their behavior. 
+If the student has rushed through tasks, make them feel guilty. If they have done a good job, appreciate them but still remind them about deep learning and mastery. 
+MAKE SURE YOU VERY STRICTLY FOLLOW THE OUTPUT STRUCTURE THAT IS 'AI Insight Heading:AI Insight Description'"""
+
+@app.post("/generate_insight")
+async def generate_insight(request: RoadmapLogs):
+    prompt = f""" I am giving you some logs for a roadmap feature {request.roadmap_logs}. Your task is to analyze them and answer my queries.
+                  'Checked' means the task is completed.
+                  Make sure to include the topic details that they have left out.
+                  Always start with a rhetorical question in the case of negative feedback and a compliment in case of positive feedback. 
+                  Make sure to instill a sense of guilt in them while also acting as a benevolent guide.
+                  Make sure the insight reflects their approach, score and behavior accordingly.
+                  MAKE SURE YOU THAT YOU GIVE ME ONLY TWO THINGS 1) AI Insight Heading and 2) The AI Insight description.
+                  MAKE SURE YOU FOLLOW THE FOLLOWING OUTPUT STRCUTURE:
+                  
+                  The heading should be a very short 2,3 line summary of the description
+                  output_structure
+                  'AI Insight Heading:AI Insight Description'
+              """
+    # Call OpenAI API
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": role},
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    # Extract and return the insight
+    answer = response['choices'][0]['message']['content'].strip()
+    return {"AI_Insight": answer}
+