✅ Final: Updated app.py and cleaned requirements.txt

app.py

@@ -65,7 +65,8 @@ def process_file(file):
     try:
         ext = os.path.splitext(file.name)[1].lower()
         with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp:
-            tmp.write(file.read())
+            file_bytes = file.read() if hasattr(file, "read") else file
+            tmp.write(file_bytes)
             tmp.flush()
             full_text = extract_text(tmp.name, ext)
 
@@ -114,17 +115,17 @@ def detect_question_type(q):
 def post_process_output(answer_text, question):
     qtype = detect_question_type(question)
     label_map = {
-        "definition": "📘 **Definition**",
-        "explanation": "📘 **Explanation**",
-        "comparison": "📘 **Comparison**",
-        "list": "📘 **Key Points**",
-        "general": "📘 **Insight**",
+        "definition": "\ud83d\udcd8 **Definition**",
+        "explanation": "\ud83d\udcd8 **Explanation**",
+        "comparison": "\ud83d\udcd8 **Comparison**",
+        "list": "\ud83d\udcd8 **Key Points**",
+        "general": "\ud83d\udcd8 **Insight**",
     }
     answer_text = f"{label_map.get(qtype)}\n\n{answer_text}"
 
     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()}"
+        answer_text += f"\n\n\ud83d\udcdd **Summary:** {summary.strip()}"
 
     return answer_text
 
@@ -150,24 +151,31 @@ def ask_question(question):
     return post_process_output(result.strip(), question)
 
 # Gradio UI
-title = "📚 AI Study Assistant"
-with gr.Blocks(css="footer {display:none !important}") as demo:
-    gr.Markdown("""# 📘 AI Study Assistant
-Upload your lecture notes and ask deep academic questions. Powered by Phi-3 & FAISS.""")
-
-    with gr.Row():
-        file_input = gr.File(label="Upload Course Material (PDF, DOCX, TXT, PPTX)")
-        upload_btn = gr.Button("Process File")
-
-    status = gr.Textbox(label="Status", interactive=False)
-
-    question = gr.Textbox(label="Ask a Question", placeholder="E.g., What is demand paging?")
-    ask_btn = gr.Button("Get Answer")
-    answer = gr.Markdown("", elem_id="answer-box")
-
-    upload_btn.click(fn=process_file, inputs=file_input, outputs=status)
-    ask_btn.click(fn=ask_question, inputs=question, outputs=answer)
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("""
+    # 📚 AI Study Assistant
+    Upload your lecture slide/text file, ask questions, and get intelligent answers powered by Phi-3.
+    """)
+
+    with gr.Tab("Upload & Ask"):
+        with gr.Row():
+            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)
+
+        gr.Markdown("---")
+
+        with gr.Row():
+            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)
+
+    with gr.Tab("History"):
+        gr.Markdown("""
+        **⏳ Coming Soon**: Question-answer history, summarization view, more!
+        """)
 
 if __name__ == "__main__":
     demo.launch()
-

requirements.txt

@@ -1,6 +1,5 @@
 transformers
 torch
-pypdf
 faiss-cpu
 python-pptx
 python-docx