Final: Updated app.py and requirements for production

app.py

@@ -94,23 +94,17 @@ def process_file(file):
         return f"❌ Error: {str(e)}"
 
 def generate_prompt(context, question):
-    return f"""
-You are a helpful academic tutor assisting a student strictly based on course slides or textbook material.
-
-Context:
+    return f"""Context:
 {context}
 
 Question:
 {question}
 
 Instructions:
-- Answer ONLY using the above context. Do NOT add outside knowledge.
-- Think clearly and deeply before answering.
-- Use structured academic language based strictly on the context.
-- Use clean formatting with helpful headings and minimal bullet points.
-- Do NOT repeat the question or include prompt labels.
-- If the context lacks an answer, say: "The provided material does not contain sufficient information to answer this question accurately."
-- Output must be academically concise, well-organized, and visually clear.
+- Answer ONLY using the above context.
+- Use structured academic language.
+- Think carefully and answer concisely.
+- If context lacks information, say: "The material does not contain enough information to answer this accurately."
 """.strip()
 
 def detect_question_type(q):
@@ -134,13 +128,17 @@ def post_process_output(answer_text, question):
         "list": "📘 **Key Points**",
         "general": "📘 **Insight**",
     }
-    answer_text = f"{label_map.get(qtype)}\n\n{answer_text}"
+    clean_answer = answer_text.strip()
+
+    if clean_answer.lower().startswith("context:") or "instructions:" in clean_answer:
+        for marker in ["Context:", "Question:", "Instructions:"]:
+            clean_answer = clean_answer.replace(marker, "").strip()
 
-    if len(answer_text.split()) > 80:
-        summary = summary_pipeline(answer_text, max_length=60, min_length=25, do_sample=False)[0]['summary_text']
-        answer_text += f"\n\n📝 **Summary:** {summary.strip()}"
+    if len(clean_answer.split()) > 80:
+        summary = summary_pipeline(clean_answer, max_length=60, min_length=25, do_sample=False)[0]['summary_text']
+        clean_answer += f"\n\n📝 **Summary:** {summary.strip()}"
 
-    return answer_text
+    return f"{label_map.get(qtype)}\n\n{clean_answer}"
 
 def ask_question(question):
     global vectorstore
@@ -155,14 +153,7 @@ def ask_question(question):
     prompt = generate_prompt(context, question)
     result = reasoning_pipeline(prompt)[0]['generated_text']
 
-    if result.startswith("You are a helpful academic tutor"):
-        result = result.split("Instructions:")[-1].strip()
-    for marker in ["Context:", "Question:", "Instructions:"]:
-        result = result.replace(marker, "").strip()
-    if "." in result:
-        result = result.rsplit(".", 1)[0] + "."
-
-    return post_process_output(result.strip(), question)
+    return post_process_output(result, question)
 
 # Gradio UI
 with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
@@ -176,7 +167,7 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             file_input = gr.File(label="📄 Upload File", file_types=[".pdf", ".docx", ".pptx", ".txt"])
             upload_btn = gr.Button("Upload")
         upload_output = gr.Textbox(label="Upload Status", interactive=False)
-        upload_btn.click(fn=process_file, inputs=file_input, outputs=upload_output)
+        upload_btn.click(fn=process_file, inputs=[file_input], outputs=[upload_output])
 
         gr.Markdown("---")
 
@@ -184,11 +175,11 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             question = gr.Textbox(label="❓ Ask a question")
             ask_btn = gr.Button("Ask")
         answer = gr.Textbox(label="💡 Answer", interactive=False)
-        ask_btn.click(fn=ask_question, inputs=question, outputs=answer)
+        ask_btn.click(fn=ask_question, inputs=[question], outputs=[answer])
 
     with gr.Tab("History"):
         gr.Markdown("""
-        **⏳ Coming Soon**: Question-answer history, summarization view, more!
+        **⏳ Coming Soon**: Question-answer history, summarization view, and more!
         """)
 
 if __name__ == "__main__":