Update app.py

app.py

@@ -11,7 +11,7 @@ from datetime import datetime
 import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
-# Configuration and setup
+# Setup paths
 persistent_dir = "/data/hf_cache"
 os.makedirs(persistent_dir, exist_ok=True)
 
@@ -38,10 +38,6 @@ MAX_NEW_TOKENS = 2048
 PROMPT_OVERHEAD = 500
 
 def clean_response(text: str) -> str:
-    try:
-        text = text.encode('utf-8', 'surrogatepass').decode('utf-8')
-    except UnicodeError:
-        text = text.encode('utf-8', 'replace').decode('utf-8')
     text = re.sub(r"\[.*?\]|\bNone\b", "", text, flags=re.DOTALL)
     text = re.sub(r"\n{3,}", "\n\n", text)
     text = re.sub(r"[^\n#\-\*\w\s\.,:\(\)]+", "", text)
@@ -52,35 +48,28 @@ def estimate_tokens(text: str) -> int:
 
 def extract_text_from_excel(file_path: str) -> str:
     all_text = []
-    try:
-        xls = pd.ExcelFile(file_path)
-        for sheet_name in xls.sheet_names:
-            df = xls.parse(sheet_name)
-            df = df.astype(str).fillna("")
-            rows = df.apply(lambda row: " | ".join(row), axis=1)
-            sheet_text = [f"[{sheet_name}] {line}" for line in rows]
-            all_text.extend(sheet_text)
-    except Exception as e:
-        raise ValueError(f"Failed to extract text from Excel file: {str(e)}")
+    xls = pd.ExcelFile(file_path)
+    for sheet_name in xls.sheet_names:
+        df = xls.parse(sheet_name).astype(str).fillna("")
+        rows = df.apply(lambda row: " | ".join(row), axis=1)
+        sheet_text = [f"[{sheet_name}] {line}" for line in rows]
+        all_text.extend(sheet_text)
     return "\n".join(all_text)
 
 def split_text_into_chunks(text: str, max_tokens: int = MAX_CHUNK_TOKENS) -> List[str]:
-    effective_max_tokens = max_tokens - PROMPT_OVERHEAD
-    if effective_max_tokens <= 0:
-        raise ValueError("Effective max tokens must be positive.")
-    lines = text.split("\n")
-    chunks, current_chunk, current_tokens = [], [], 0
+    effective_max = max_tokens - PROMPT_OVERHEAD
+    lines, chunks, curr_chunk, curr_tokens = text.split("\n"), [], [], 0
     for line in lines:
-        line_tokens = estimate_tokens(line)
-        if current_tokens + line_tokens > effective_max_tokens:
-            if current_chunk:
-                chunks.append("\n".join(current_chunk))
-            current_chunk, current_tokens = [line], line_tokens
+        t = estimate_tokens(line)
+        if curr_tokens + t > effective_max:
+            if curr_chunk:
+                chunks.append("\n".join(curr_chunk))
+            curr_chunk, curr_tokens = [line], t
         else:
-            current_chunk.append(line)
-            current_tokens += line_tokens
-    if current_chunk:
-        chunks.append("\n".join(current_chunk))
+            curr_chunk.append(line)
+            curr_tokens += t
+    if curr_chunk:
+        chunks.append("\n".join(curr_chunk))
     return chunks
 
 def build_prompt_from_text(chunk: str) -> str:
@@ -99,128 +88,86 @@ Analyze the following clinical notes and provide a detailed, concise summary foc
 {chunk}
 
 ---
+
 Respond in well-structured bullet points with medical reasoning.
 """
 
 def init_agent():
-    default_tool_path = os.path.abspath("data/new_tool.json")
-    target_tool_path = os.path.join(tool_cache_dir, "new_tool.json")
-    if not os.path.exists(target_tool_path):
-        shutil.copy(default_tool_path, target_tool_path)
+    tool_path = os.path.join(tool_cache_dir, "new_tool.json")
+    if not os.path.exists(tool_path):
+        shutil.copy(os.path.abspath("data/new_tool.json"), tool_path)
     agent = TxAgent(
         model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
         rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
-        tool_files_dict={"new_tool": target_tool_path},
+        tool_files_dict={"new_tool": tool_path},
         force_finish=True,
         enable_checker=True,
         step_rag_num=4,
-        seed=100,
-        additional_default_tools=[]
+        seed=100
     )
     agent.init_model()
     return agent
 
 def process_final_report(agent, file, chatbot_state: List[Dict[str, str]]) -> Tuple[List[Dict[str, str]], Union[str, None]]:
     messages = chatbot_state if chatbot_state else []
-    report_path = None
-
     if file is None or not hasattr(file, "name"):
-        messages.append({"role": "assistant", "content": "❌ Please upload a valid Excel file before analyzing."})
-        return messages, report_path
-
-    try:
-        messages.append({"role": "user", "content": f"Processing Excel file: {os.path.basename(file.name)}"})
-        extracted_text = extract_text_from_excel(file.name)
-        chunks = split_text_into_chunks(extracted_text)
-        chunk_responses = [None] * len(chunks)
-
-        def analyze_chunk(index: int, chunk: str) -> Tuple[int, str]:
-            prompt = build_prompt_from_text(chunk)
-            prompt_tokens = estimate_tokens(prompt)
-            if prompt_tokens > MAX_MODEL_TOKENS:
-                return index, f"❌ Chunk {index+1} prompt too long. Skipping..."
-            response = ""
-            try:
-                for result 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(result, str):
-                        response += result
-                    elif isinstance(result, list):
-                        for r in result:
-                            if hasattr(r, "content"):
-                                response += r.content
-                    elif hasattr(result, "content"):
-                        response += result.content
-            except Exception as e:
-                return index, f"❌ Error analyzing chunk {index+1}: {str(e)}"
-            return index, clean_response(response)
-
-        with ThreadPoolExecutor(max_workers=1) as executor:
-            futures = [executor.submit(analyze_chunk, i, chunk) for i, chunk in enumerate(chunks)]
-            for future in as_completed(futures):
-                i, result = future.result()
-                chunk_responses[i] = result
-                if result.startswith("❌"):
-                    messages.append({"role": "assistant", "content": result})
-
-        valid_responses = [res for res in chunk_responses if not res.startswith("❌")]
-        if not valid_responses:
-            messages.append({"role": "assistant", "content": "❌ No valid chunk responses to summarize."})
-            return messages, report_path
-
-        summary = "\n\n".join(valid_responses)
-        final_prompt = f"Provide a structured, consolidated clinical analysis from these results:\n\n{summary}"
-        messages.append({"role": "assistant", "content": "📊 Generating final report..."})
-
-        final_report_text = ""
-        for result in agent.run_gradio_chat(
-            message=final_prompt,
-            history=[],
-            temperature=0.2,
-            max_new_tokens=MAX_NEW_TOKENS,
-            max_token=MAX_MODEL_TOKENS,
-            call_agent=False,
-            conversation=[],
-        ):
-            if isinstance(result, str):
-                final_report_text += result
-            elif isinstance(result, list):
-                for r in result:
+        return messages + [{"role": "assistant", "content": "❌ Please upload a valid Excel file."}], None
+
+    messages.append({"role": "user", "content": f"Processing Excel file: {os.path.basename(file.name)}"})
+    text = extract_text_from_excel(file.name)
+    chunks = split_text_into_chunks(text)
+    chunk_responses = [None] * len(chunks)
+
+    def analyze_chunk(i, chunk):
+        prompt = build_prompt_from_text(chunk)
+        response = ""
+        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):
+                response += res
+            elif hasattr(res, "content"):
+                response += res.content
+            elif isinstance(res, list):
+                for r in res:
                     if hasattr(r, "content"):
-                        final_report_text += r.content
-            elif hasattr(result, "content"):
-                final_report_text += result.content
-
-        cleaned = clean_response(final_report_text)
-        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}")
-
-        messages.append({"role": "assistant", "content": f"📊 Final Report:\n\n{cleaned}"})
-        messages.append({"role": "assistant", "content": f"✅ Report generated and saved: {os.path.basename(report_path)}"})
-
-    except Exception as e:
-        messages.append({"role": "assistant", "content": f"❌ Error processing file: {str(e)}"})
-
+                        response += r.content
+        return i, clean_response(response)
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        futures = [executor.submit(analyze_chunk, i, c) for i, c in enumerate(chunks)]
+        for f in as_completed(futures):
+            i, result = f.result()
+            chunk_responses[i] = result
+
+    valid = [r for r in chunk_responses if r and not r.startswith("❌")]
+    if not valid:
+        return messages + [{"role": "assistant", "content": "❌ No valid chunk results."}], None
+
+    summary_prompt = f"Summarize this analysis in a final structured report:\n\n" + "\n\n".join(valid)
+    messages.append({"role": "assistant", "content": "📊 Generating final report..."})
+
+    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)
+    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}")
+
+    messages.append({"role": "assistant", "content": f"📊 Final Report:\n\n{cleaned}"})
+    messages.append({"role": "assistant", "content": f"✅ Report generated and saved: {os.path.basename(report_path)}"})
     return messages, report_path
 
 def create_ui(agent):
-    with gr.Blocks(css="""
+    with gr.Blocks(css=\"\"\"
         html, body, .gradio-container {
             height: 100vh;
-            width: 100vw;
-            margin: 0;
-            padding: 0;
-            font-family: 'Inter', sans-serif;
             background-color: #111827;
             color: #e5e7eb;
+            font-family: 'Inter', sans-serif;
         }
         .gr-button.primary {
             background: #2563eb;
@@ -234,30 +181,33 @@ def create_ui(agent):
         }
         .gr-chatbot {
             background-color: #1f2937;
-            color: #e5e7eb;
+            border: 1px solid #374151;
             border-radius: 10px;
             padding: 1rem;
-            border: 1px solid #374151;
         }
-        .gr-markdown, .gr-file-upload {
+        .gr-file-upload {
             background-color: #1f2937;
-            border-radius: 10px;
-            box-shadow: 0 0 10px rgba(0,0,0,0.2);
             border: 1px solid #374151;
+            border-radius: 8px;
         }
-    """) as demo:
-        gr.Markdown("""
-        <h2 style='color:#60a5fa'>🩺 Patient History AI Assistant</h2>
-        <p style='color:#cbd5e1'>Upload a clinical Excel file and receive a structured diagnostic summary.</p>
-        """)
-
+    \"\"\") as demo:
+        gr.Markdown(\"\"\"<h2 style='color:#60a5fa'>🩺 Patient History AI Assistant</h2><p>Upload a clinical Excel file and receive a structured diagnostic summary.</p>\"\"\")
         with gr.Row():
             with gr.Column(scale=3):
-                chatbot = gr.Chatbot(label="Clinical Assistant", height=700, type="messages")
+                chatbot = gr.Chatbot(
+                    label=\"Clinical Assistant\",
+                    height=700,
+                    type=\"messages\",
+                    avatar_images=[
+                        \"https://ui-avatars.com/api/?name=AI&background=2563eb&color=fff&size=128\",
+                        \"https://ui-avatars.com/api/?name=You&background=374151&color=fff&size=128\"
+                    ]
+                )
             with gr.Column(scale=1):
-                file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"])
-                analyze_btn = gr.Button("🧠 Analyze", variant="primary")
-                report_output = gr.File(label="Download Report", visible=False, interactive=False)
+                with gr.Row():
+                    file_upload = gr.File(label=\"\", file_types=[\".xlsx\"], elem_id=\"upload-btn\")
+                    analyze_btn = gr.Button(\"🧠 Analyze\", variant=\"primary\")
+                report_output = gr.File(label=\"Download Report\", visible=False, interactive=False)
 
         chatbot_state = gr.State(value=[])
 
@@ -269,11 +219,11 @@ def create_ui(agent):
 
     return demo
 
-if __name__ == "__main__":
+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=False)
     except Exception as e:
-        print(f"Error: {str(e)}")
+        print(f\"Error: {str(e)}\")
         sys.exit(1)