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Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, UploadFile, Form, HTTPException
+from pydantic import BaseModel
+import uvicorn
+from fastapi.responses import JSONResponse
+from typing import Dict
+import hashlib
+from openai import OpenAI
+from dotenv import load_dotenv
+import os
+load_dotenv()
+client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+# from pathlib import Path
+# from langchain_community.document_loaders import WebBaseLoade as genai
+import os
+import re
+import pandas as pd
+from fastapi.middleware.cors import CORSMiddleware
+from firebase_admin import firestore
+import json
+import google.generativeai as genai
+from google.generativeai import GenerativeModel
+
+# Initialize Gemini LLM
+# load_dotenv()
+# Google_key = os.getenv("GOOGLE_API_KEY")
+# print(str(Google_key))
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+model = genai.GenerativeModel("gemini-2.0-flash")
+import firebase_admin
+from firebase_admin import credentials
+
+# cred = credentials.Certificate("/content/ir-502e5-firebase-adminsdk-3der0-0145a61d7a.json")
+# firebase_admin.initialize_app(cred)
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+def generate_df():
+    data = []
+    cred = credentials.Certificate("G:/Cognozire/Alguru/Feeback_Api's/fir-502e5-firebase-adminsdk-3der0-0145a61d7a.json")
+    firebase_admin.initialize_app(cred)
+    db = firestore.client()
+    docs = db.collection("test_results").get()
+    for doc in docs:
+         doc_data = doc.to_dict()
+         doc_data['id'] = doc.id
+         data.append(doc_data)
+    df = pd.DataFrame(data)
+    return df
+
+def generate_feedback(email, test_id):
+    df = generate_df()
+    df_email = df[df['email'] == email]
+    df_test_id = df_email[df_email['id'] == test_id]
+    if not df_test_id.empty:
+        response = df_test_id['responses'].values[0]
+        feedback = model.generate_content(f"""You are an experienced tutor analyzing a student's test responses to provide constructive feedback. Below is the student's test history in JSON format. Your task is to:
+
+Identify Strengths: Highlight areas where the student performed well, demonstrating a strong understanding of the concepts.
+
+Identify Weaknesses: Point out areas where the student struggled or made consistent errors, indicating gaps in understanding.
+
+Provide Actionable Suggestions: Offer specific advice on how the student can improve their performance in future tests.
+
+Encourage and Motivate: End with positive reinforcement to keep the student motivated.
+Test History:{str(response)} """)
+        return feedback.text
+    else:
+        print("No test results found for this id")
+def generate_overall_feedback(email):
+    df = generate_df()
+    df_email = df[df['email'] == email]
+    if not df_email.empty:
+        response = df_email['responses'].values 
+        feedback = model.generate_content(f"""You are an experienced tutor analyzing a student's test responses to provide constructive feedback. Below is the student's test history in list format. Your task is to:
+                                          Identify Strengths: Highlight areas where the student performed well, demonstrating a strong understanding of the concepts.
+
+Identify Weaknesses: Point out areas where the student struggled or made consistent errors, indicating gaps in understanding.
+
+Provide Actionable Suggestions: Offer specific advice on how the student can improve their performance in future tests.
+
+Encourage and Motivate: End with positive reinforcement to keep the student motivated.
+
+Test History:{str(response)} """)
+        return feedback.text
+    else:
+        print("Please try again with a valid email")
+    
+
+
+
+
+@app.post("/get_single_feedback")
+async def get_single_feedback(email: str, test_id: str):
+    feedback = generate_feedback(email, test_id)
+    return JSONResponse(content={"feedback": feedback})
+
+@app.post("/get_overall_feedback")
+async def get_overall_feedback(email: str):
+    feedback = generate_overall_feedback(email)
+    return JSONResponse(content={"feedback": feedback})
+
+@app.post("/get_strong_weak_topics")
+async def get_strong_weak_topics(email: str):   
+    df = generate_df()
+    df_email = df[df['email'] == email]
+    if not df_email.empty:
+        response = df_email['responses'].values
+        # Assuming response is a list of responses
+        formatted_data = str(response)  # Convert response to a string format suitable for the API call
+        section_info = {
+            'filename': 'student_performance',
+            'schema': {
+                'weak_topics': ['Topic#1', 'Topic#2', '...'],
+                'strong_topics': ['Topic#1', 'Topic#2', '...']
+            }
+        }
+        
+        # Generate response using the client
+        completion = client.chat.completions.create(
+            model="gpt-4o",
+            response_format={"type": "json_object"},
+            messages=[
+                {
+                    "role": "system",
+                    "content": f"""You are an Educational Performance Analyst focusing on {section_info['filename'].replace('_', ' ')}.
+                    Analyze the provided student responses to identify and categorize topics into 'weak' and 'strong' based on their performance. Try to give
+                    high level topics like algebra, trignometry, geometry etc in your response.
+                    Do not add any explanations, introduction, or comments - return ONLY valid JSON.
+                    """
+                },
+                {
+                    "role": "user",
+                    "content": f"""
+                    Here is the raw data for {section_info['filename']}:
+                    
+                    {formatted_data}
+                    
+                    Convert this data into JSON that matches this schema:
+                    {json.dumps(section_info['schema'], indent=2)}
+                    """
+                }
+            ],
+            temperature=0.0
+        )
+        
+        # Extract the JSON content from the completion object
+        strong_weak_topics = completion.choices[0].message.content  # Access the content attribute directly
+        
+        return JSONResponse(content=json.loads(strong_weak_topics))
+    else:
+        return JSONResponse(content={"error": "No test results found for this email"})
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)