Update app.py

app.py

@@ -44,7 +44,10 @@ def estimate_tokens(text: str) -> int:
 
 def extract_text_from_excel(file_path: str) -> str:
     all_text = []
-    xls = pd.ExcelFile(file_path)
+    try:
+        xls = pd.ExcelFile(file_path)
+    except Exception as e:
+        raise ValueError(f"❌ Error reading Excel file: {e}")
     for sheet_name in xls.sheet_names:
         df = xls.parse(sheet_name).astype(str).fillna("")
         rows = df.apply(lambda row: " | ".join([cell for cell in row if cell.strip()]), axis=1)
@@ -105,64 +108,55 @@ def init_agent():
     agent.init_model()
     return agent
 
-def stream_report(agent, file: gr.File, chatbot_state: List[Tuple[str, str]]) -> Generator:
-    messages = chatbot_state or []
-    if file is None or not hasattr(file, "name"):
-        yield messages + [("assistant", "❌ Please upload a valid Excel file.")], None, ""
-        return
-
-    messages.append(("user", f"📎 Uploaded file: {os.path.basename(file.name)}"))
-    yield messages, None, ""
-
-    text = extract_text_from_excel(file.name)
-    chunks = split_text_into_chunks(text)
-    chunk_responses = []
-
-    for i, chunk in enumerate(chunks):
-        prompt = build_prompt_from_text(chunk)
-        partial = ""
+def stream_report(agent, file: gr.File, full_output: str) -> Generator:
+    accumulated_text = ""
+    try:
+        if file is None:
+            yield "❌ Please upload a valid Excel file.", None, ""
+            return
+
+        filepath = file.name if hasattr(file, "name") else file
+        text = extract_text_from_excel(filepath)
+        chunks = split_text_into_chunks(text)
+
+        for i, chunk in enumerate(chunks):
+            prompt = build_prompt_from_text(chunk)
+            partial = ""
+            for res in agent.run_gradio_chat(
+                message=prompt, history=[], temperature=0.2,
+                max_new_tokens=MAX_NEW_TOKENS, max_token=MAX_MODEL_TOKENS,
+                call_agent=False, conversation=[]
+            ):
+                if isinstance(res, str):
+                    partial += res
+                elif hasattr(res, "content"):
+                    partial += res.content
+            cleaned = clean_response(partial)
+            accumulated_text += f"\n\n📄 **Chunk {i+1}**:\n{cleaned}"
+            yield accumulated_text, None, ""
+
+        summary_prompt = f"Summarize this analysis in a final structured report:\n\n" + accumulated_text
+        final_report = ""
         for res in agent.run_gradio_chat(
-            message=prompt, history=[], temperature=0.2,
+            message=summary_prompt, history=[], temperature=0.2,
             max_new_tokens=MAX_NEW_TOKENS, max_token=MAX_MODEL_TOKENS,
             call_agent=False, conversation=[]
         ):
             if isinstance(res, str):
-                partial += res
+                final_report += res
             elif hasattr(res, "content"):
-                partial += res.content
-        cleaned = clean_response(partial)
-        messages.append(("assistant", f"📄 Chunk {i+1}:\n\n{cleaned}"))
-        chunk_responses.append(cleaned)
-        yield messages, None, ""
-
-    valid = [r for r in chunk_responses if r and not r.startswith("❌")]
-    if not valid:
-        messages.append(("assistant", "❌ No valid results found in the file."))
-        yield messages, None, ""
-        return
-
-    summary_prompt = f"Summarize this analysis in a final structured report:\n\n" + "\n\n".join(valid)
-    messages.append(("assistant", "📊 Generating final summary..."))
-    yield messages, None, ""
-
-    final_report = ""
-    for res in agent.run_gradio_chat(
-        message=summary_prompt, history=[], temperature=0.2,
-        max_new_tokens=MAX_NEW_TOKENS, max_token=MAX_MODEL_TOKENS,
-        call_agent=False, conversation=[]
-    ):
-        if isinstance(res, str):
-            final_report += res
-        elif hasattr(res, "content"):
-            final_report += res.content
-
-    cleaned = clean_response(final_report)
-    messages.append(("assistant", cleaned))
-    report_path = os.path.join(report_dir, f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md")
-    with open(report_path, 'w') as f:
-        f.write(f"# 🧠 Final Patient Report\n\n{cleaned}")
-
-    yield messages, report_path, cleaned
+                final_report += res.content
+
+        cleaned = clean_response(final_report)
+        accumulated_text += f"\n\n📊 **Final Summary**:\n{cleaned}"
+        report_path = os.path.join(report_dir, f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md")
+        with open(report_path, 'w') as f:
+            f.write(f"# 🧠 Final Patient Report\n\n{cleaned}")
+
+        yield accumulated_text, report_path, cleaned
+
+    except Exception as e:
+        yield f"❌ Error: {str(e)}", None, ""
 
 def create_ui(agent):
     with gr.Blocks(css="""
@@ -180,11 +174,11 @@ def create_ui(agent):
             border-radius: 0;
             background-color: #1a1f2e;
         }
-        .chatbot {
+        .output-markdown {
             background-color: #131720;
             border-radius: 12px;
             padding: 20px;
-            height: 600px;
+            min-height: 600px;
             overflow-y: auto;
             border: 1px solid #2c3344;
         }
@@ -204,17 +198,16 @@ def create_ui(agent):
         gr.Markdown("""# 🧠 Clinical Reasoning Assistant
 Upload clinical Excel records below and click **Analyze** to generate a medical summary.
 """)
-        chatbot = gr.Chatbot(label="Chatbot", elem_classes="chatbot", type="tuples")
-        report_output_markdown = gr.Markdown(visible=False)
         file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"])
         analyze_btn = gr.Button("Analyze")
-        report_output = gr.File(label="Download Report", visible=False)
-        chatbot_state = gr.State(value=[])
+        report_output_markdown = gr.Markdown(elem_classes="output-markdown")
+        report_file = gr.File(label="Download Report", visible=False)
+        full_output = gr.State(value="")
 
         analyze_btn.click(
             fn=stream_report,
-            inputs=[file_upload, chatbot_state],
-            outputs=[chatbot, report_output, report_output_markdown]
+            inputs=[file_upload, full_output],
+            outputs=[report_output_markdown, report_file, full_output]
         )
 
     return demo
@@ -223,7 +216,7 @@ if __name__ == "__main__":
     try:
         agent = init_agent()
         demo = create_ui(agent)
-        demo.launch(server_name="0.0.0.0", server_port=7860, allowed_paths=["/data/hf_cache/reports"], share=False)
+        demo.launch(server_name="0.0.0.0", server_port=7860, allowed_paths=["/data/hf_cache/reports"], share=True)
     except Exception as e:
         print(f"Error: {str(e)}")
         sys.exit(1)