Upload main.py

main.py

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+# main.py
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Dict
+import os
+from groq import Groq
+
+app = FastAPI()
+
+# Pydantic model for request
+class ScoreInput(BaseModel):
+    score_percentages: Dict[str, float]
+    time_percentages: Dict[str, float]
+
+# Helper functions
+def get_final_score(score_percentages: Dict[str, float], time_percentages: Dict[str, float]) -> Dict[str, float]:
+    final_score = {}
+    for skill in score_percentages:
+        score_avg = (score_percentages[skill] + time_percentages[skill]) / 2
+        final_score[skill] = score_avg
+    return final_score
+
+def get_strengths_and_weaknesses(final_score: Dict[str, float]):
+    sorted_skills = sorted(
+        [(skill, score) for skill, score in final_score.items()],
+        key=lambda item: item[1],
+        reverse=True
+    )
+    num_skills = len(sorted_skills)
+    if num_skills == 0:
+        return [], [], []
+    
+    split1 = num_skills // 3
+    split2 = 2 * (num_skills // 3)
+    
+    strengths = sorted_skills[:split1]
+    opportunities = sorted_skills[split1:split2]
+    challenges = sorted_skills[split2:]
+    
+    return strengths, opportunities, challenges
+
+# FastAPI route
+@app.post("/analyze")
+async def analyze_scores(input_data: ScoreInput):
+    final_score = get_final_score(input_data.score_percentages, input_data.time_percentages)
+    strengths, opportunities, challenges = get_strengths_and_weaknesses(final_score)
+
+    # Groq API call
+    api_key = os.getenv("GROQ_API_KEY")
+    if not api_key:
+        raise HTTPException(status_code=500, detail="Groq API key not found")
+
+    client = Groq(api_key=api_key)
+    sys_prompt = f"""You are an advanced language model trained to analyze student responses from a questionnaire on Academic, Cognitive, and Study Profile aspects related to JEE Mains preparation. Your task is to generate a personalized SCO (Strengths, Challenges, Opportunities) analysis and an Action Plan section based on the user's inputs.
+    You have been provided with the strengths {strengths}, Opportunities {opportunities} and Challenges {challenges} skills of the user
+    Output Structure:
+    SCO Analysis:
+    Strengths:
+    - List the student's strengths based on their {strengths} skills
+    - Let the student now how they can use these strengths in their JEE preparation and exam to improve their score.
+    - Also tell them how do they improve their score more.
+    Opportunities:
+    - List the student's strengths based on their {opportunities} skills
+    - Suggest opportunities for improvement by leveraging the student's strengths and addressing their challenges.
+    - Recommend ways to enhance their score in the {opportunities} skills.
+    - Also tell them if they improve in these skills what opportunities they have in improving their scores
+    Challenges:
+    - List the student's strengths based on their {challenges} skills
+    - Guide the student that these skills are basically the core area where they are lacking
+    - Tell them that if they continue not focusing upon them they might get far away from their JEE goal.
+    Action Plan:
+    - Provide a detailed plan to the student to improve in the {challenges} skills.
+    - Recommend targeted strategies, resources, and techniques to improve their {challenges} skills.
+    - Let them know if they improve these areas how it can help boost their scores and make their preparation more effective.
+    - Incorporate time management, revision, and test-taking strategies specific to JEE Mains and the identified subjects/topics/subtopics.
+
+    Your analysis and action plan should be comprehensive, consistent, and tailored to the individual student's responses while leveraging your knowledge of the JEE Mains exam context, the mapping of subjects/topics to general cognitive traits and skills, and the ability to identify overarching trends across related subjects/topics."""
+
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": sys_prompt},
+                {"role": "user", "content": f"Generate the SOCA analysis based on the system prompt and {strengths}, {opportunities} and {challenges}. MAKE SURE WE STRICTLY FOLLOW THE STRUCTURE."},
+            ],
+            model="llama3-70b-8192",
+        )
+        analysis = chat_completion.choices[0].message.content
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error calling Groq API: {str(e)}")
+
+    return {"analysis": analysis}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)