Update app.py

app.py

@@ -227,6 +227,94 @@ The topics: {topic_str}
 
     return JSONResponse(content=flashcards)
 
+@app.post("/generate_detailed_summary")
+async def generate_detailed_summary(email: str):
+    df = generate_df()
+    df_email = df[df['email'] == email]
+
+    if len(df_email) < 10:
+        return JSONResponse(content={"message": "Please attempt at least 10 tests to enable detailed summary generation."})
+
+    # Step 1: Get the weak topics via DeepSeek
+    response = df_email['responses'].values[:10]
+    formatted_data = str(response)
+
+    schema = {
+        'weak_topics': ['Topic#1', 'Topic#2', '...'],
+        'strong_topics': ['Topic#1', 'Topic#2', '...']
+    }
+
+    completion = client.chat.completions.create(
+        model="deepseek-chat",
+        response_format={"type": "json_object"},
+        messages=[
+            {
+                "role": "system",
+                "content": f"""You are an Educational Performance Analyst focusing on student performance.
+                Analyze the provided student responses to identify and categorize topics into 'weak' and 'strong' based on their performance.
+                Do not add any explanations - return ONLY valid JSON."""
+            },
+            {
+                "role": "user",
+                "content": f"""
+                Here is the raw data:
+                {formatted_data}
+
+                Convert this data into JSON that matches this schema:
+                {json.dumps(schema, indent=2)}
+                """
+            }
+        ],
+        temperature=0.0
+    )
+
+    # Extract weak topics
+    strong_weak_json = json.loads(completion.choices[0].message.content)
+    weak_topics = strong_weak_json.get("weak_topics", [])
+
+    if not weak_topics:
+        return JSONResponse(content={"message": "Could not extract weak topics."})
+
+    # Step 2: Generate flashcards using Gemini
+    topic_str = ", ".join(weak_topics)
+#     flashcard_prompt = f"""Create 5 concise, simple, straightforward and distinct Anki cards to study the following topic, each with a front and back.
+# Avoid repeating the content in the front on the back of the card. Avoid explicitly referring to the author or the article.
+# Use the following format:
+# Front: [front section of card 1]
+# Back: [back section of card 1]
+# ...
+# The topics: {topic_str}
+# """
+
+#     flashcard_response = model.generate_content(flashcard_prompt)
+
+#     # Step 3: Parse Gemini response into JSON format
+#     flashcards_raw = flashcard_response.text.strip()
+#     flashcard_pattern = re.findall(r"Front:\s*(.*?)\nBack:\s*(.*?)(?=\nFront:|\Z)", flashcards_raw, re.DOTALL)
+
+#     flashcards = [{"Front": front.strip(), "Back": back.strip()} for front, back in flashcard_pattern]
+
+    summarization_prompt = f"""
+Write an informative and concise summary (approximately 200 words) for each of the following topics. 
+Do not mention the author or source. Use clear and academic language.
+
+List of topics:
+{topic_str}
+
+Use the following format:
+Topic: [topic name]
+Summary: [200-word summary]
+"""
+
+    summary_response = model.generate_content(summarization_prompt)
+    
+    # Step 3: Parse response into JSON format
+    summary_raw = summary_response.text.strip()
+    summary_pattern = re.findall(r"Topic:\s*(.*?)\nSummary:\s*(.*?)(?=\nTopic:|\Z)", summary_raw, re.DOTALL)
+    
+    summaries = [{topic.strip(): summary.strip() for topic, summary in summary_pattern}]
+
+    return JSONResponse(content=summaries)
 
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)