Update app.py

app.py

@@ -3,14 +3,15 @@ import os
 import pandas as pd
 import json
 import gradio as gr
-from typing import List, Tuple, Union, Generator
+from typing import List, Tuple, Dict, Any, Union
 import hashlib
 import shutil
 import re
 from datetime import datetime
+import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
-# Setup directories
+# Configuration and setup
 persistent_dir = "/data/hf_cache"
 os.makedirs(persistent_dir, exist_ok=True)
 
@@ -19,21 +20,29 @@ tool_cache_dir = os.path.join(persistent_dir, "tool_cache")
 file_cache_dir = os.path.join(persistent_dir, "cache")
 report_dir = os.path.join(persistent_dir, "reports")
 
-for d in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir]:
-    os.makedirs(d, exist_ok=True)
+for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir]:
+    os.makedirs(directory, exist_ok=True)
 
 os.environ["HF_HOME"] = model_cache_dir
 os.environ["TRANSFORMERS_CACHE"] = model_cache_dir
 
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "src")))
+current_dir = os.path.dirname(os.path.abspath(__file__))
+src_path = os.path.abspath(os.path.join(current_dir, "src"))
+sys.path.insert(0, src_path)
+
 from txagent.txagent import TxAgent
 
+# Constants
 MAX_MODEL_TOKENS = 32768
 MAX_CHUNK_TOKENS = 8192
 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)
@@ -42,126 +51,286 @@ def clean_response(text: str) -> str:
 def estimate_tokens(text: str) -> int:
     return len(text) // 3.5 + 1
 
-def extract_text_from_excel(file_obj: Union[str, os.PathLike, 'file']) -> str:
+def extract_text_from_excel(file_path: str) -> str:
     all_text = []
     try:
-        xls = pd.ExcelFile(file_obj)
+        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"❌ 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)
-        sheet_text = [f"[{sheet_name}] {line}" for line in rows if line.strip()]
-        all_text.extend(sheet_text)
+        raise ValueError(f"Failed to extract text from Excel file: {str(e)}")
     return "\n".join(all_text)
 
-def split_text_into_chunks(text: str, max_tokens: int = MAX_CHUNK_TOKENS, max_chunks: int = 30) -> List[str]:
-    effective_max = max_tokens - PROMPT_OVERHEAD
-    lines, chunks, curr_chunk, curr_tokens = text.split("\n"), [], [], 0
+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(f"Effective max tokens ({effective_max_tokens}) must be positive.")
+    lines = text.split("\n")
+    chunks, current_chunk, current_tokens = [], [], 0
     for line in lines:
-        t = estimate_tokens(line)
-        if curr_tokens + t > effective_max:
-            if curr_chunk:
-                chunks.append("\n".join(curr_chunk))
-            if len(chunks) >= max_chunks:
-                break
-            curr_chunk, curr_tokens = [line], t
+        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
         else:
-            curr_chunk.append(line)
-            curr_tokens += t
-    if curr_chunk and len(chunks) < max_chunks:
-        chunks.append("\n".join(curr_chunk))
+            current_chunk.append(line)
+            current_tokens += line_tokens
+    if current_chunk:
+        chunks.append("\n".join(current_chunk))
     return chunks
 
 def build_prompt_from_text(chunk: str) -> str:
     return f"""
 ### Unstructured Clinical Records
 
-Analyze the following clinical notes and provide a detailed, concise summary focusing on:
+You are reviewing unstructured, mixed-format clinical documentation from various forms, tables, and sheets.
+
+**Objective:** Identify patterns, missed diagnoses, inconsistencies, and follow-up gaps.
+
+Here is the extracted content chunk:
+
+{chunk}
+
+Please analyze the above and provide:
 - Diagnostic Patterns
 - Medication Issues
 - Missed Opportunities
 - Inconsistencies
 - Follow-up Recommendations
-
----
-
-{chunk}
-
----
-Respond in well-structured bullet points with medical reasoning.
 """
 
 def init_agent():
-    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)
+    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)
     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": tool_path},
+        tool_files_dict={"new_tool": target_tool_path},
         force_finish=True,
         enable_checker=True,
         step_rag_num=4,
-        seed=100
+        seed=100,
+        additional_default_tools=[]
     )
     agent.init_model()
     return agent
 
-def stream_report(agent, file: Union[str, 'file'], full_output: str) -> Generator[Tuple[str, Union[str, None], str], None, None]:
-    yield from stream_report_wrapper(agent)(file, full_output)
+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)}"})
+        messages.append({"role": "assistant", "content": "⏳ Extracting and analyzing data..."})
+        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 ({prompt_tokens} tokens). 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 hasattr(result, "content"):
+                        response += result.content
+                    elif isinstance(result, list):
+                        for r in result:
+                            if hasattr(r, "content"):
+                                response += r.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 not result.startswith("❌"):
+                    messages.append({"role": "assistant", "content": f"✅ Chunk {i+1} analysis complete"})
+                else:
+                    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 = ""
+        current_summary_tokens = 0
+        for i, response in enumerate(valid_responses):
+            response_tokens = estimate_tokens(response)
+            if current_summary_tokens + response_tokens > MAX_MODEL_TOKENS - PROMPT_OVERHEAD - MAX_NEW_TOKENS:
+                summary_prompt = f"Summarize the following analysis:\n\n{summary}\n\nProvide a concise summary."
+                summary_response = ""
+                try:
+                    for result 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(result, str):
+                            summary_response += result
+                        elif hasattr(result, "content"):
+                            summary_response += result.content
+                        elif isinstance(result, list):
+                            for r in result:
+                                if hasattr(r, "content"):
+                                    summary_response += r.content
+                    summary = clean_response(summary_response)
+                    current_summary_tokens = estimate_tokens(summary)
+                except Exception as e:
+                    messages.append({"role": "assistant", "content": f"❌ Error summarizing intermediate results: {str(e)}"})
+                    return messages, report_path
+            summary += f"\n\n### Chunk {i+1} Analysis\n{response}"
+            current_summary_tokens += response_tokens
+
+        final_prompt = f"Summarize the key findings from the following analyses:\n\n{summary}"
+        messages.append({"role": "assistant", "content": "📊 Generating final report..."})
+
+        final_report_text = ""
+        try:
+            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 hasattr(result, "content"):
+                    final_report_text += result.content
+                elif isinstance(result, list):
+                    for r in result:
+                        if hasattr(r, "content"):
+                            final_report_text += r.content
+        except Exception as e:
+            messages.append({"role": "assistant", "content": f"❌ Error generating final report: {str(e)}"})
+            return messages, report_path
+
+        final_report = f"# 🧠 Final Patient Report\n\n{clean_response(final_report_text)}"
+        messages[-1]["content"] = f"📊 Final Report:\n\n{clean_response(final_report_text)}"
+
+        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+        report_path = os.path.join(report_dir, f"report_{timestamp}.md")
+
+        with open(report_path, 'w') as f:
+            f.write(final_report)
+
+        messages.append({"role": "assistant", "content": f"✅ Report generated and saved: report_{timestamp}.md"})
+
+    except Exception as e:
+        messages.append({"role": "assistant", "content": f"❌ Error processing file: {str(e)}"})
+
+    return messages, report_path
 
 def create_ui(agent):
-    with gr.Blocks(css="""
-        body {
-            background: #10141f;
-            color: #ffffff;
-            font-family: 'Inter', sans-serif;
-            margin: 0;
-            padding: 0;
-        }
+    with gr.Blocks(
+        title="Patient History Chat",
+        css="""
         .gradio-container {
-            padding: 30px;
-            width: 100vw;
-            max-width: 100%;
-            border-radius: 0;
-            background-color: #1a1f2e;
-        }
-        .output-markdown {
-            background-color: #131720;
-            border-radius: 12px;
-            padding: 20px;
-            min-height: 600px;
-            overflow-y: auto;
-            border: 1px solid #2c3344;
+            max-width: 900px !important;
+            margin: auto;
+            font-family: 'Segoe UI', sans-serif;
+            background-color: #f8f9fa;
         }
-        .gr-button {
-            background: linear-gradient(135deg, #4b4ced, #37b6e9);
+        .gr-button.primary {
+            background: linear-gradient(to right, #4b6cb7, #182848);
             color: white;
-            font-weight: 500;
             border: none;
-            padding: 10px 20px;
             border-radius: 8px;
-            transition: background 0.3s ease;
         }
-        .gr-button:hover {
-            background: linear-gradient(135deg, #37b6e9, #4b4ced);
+        .gr-button.primary:hover {
+            background: linear-gradient(to right, #3552a3, #101a3e);
         }
-    """) as demo:
-        gr.Markdown("""# 🧠 Clinical Reasoning Assistant
-Upload clinical Excel records below and click **Analyze** to generate a medical summary.
-""")
-        file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"])
-        analyze_btn = gr.Button("Analyze")
-        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, full_output],
-            outputs=[report_output_markdown, report_file, full_output]
-        )
+        .gr-file-upload, .gr-chatbot, .gr-markdown {
+            background-color: white;
+            border-radius: 10px;
+            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+            padding: 1rem;
+        }
+        .gr-chatbot {
+            border-left: 4px solid #4b6cb7;
+        }
+        .gr-file-upload input {
+            font-size: 0.95rem;
+        }
+        .chat-message-content p {
+            margin: 0.3em 0;
+        }
+        .chat-message-content ul {
+            padding-left: 1.2em;
+            margin: 0.4em 0;
+        }
+        """
+    ) as demo:
+        gr.Markdown("""
+        <h2 style='color:#182848'>🏥 Patient History Analysis Tool</h2>
+        <p style='color:#444;'>Upload an Excel file containing clinical data. The assistant will analyze it for patterns, inconsistencies, and recommendations.</p>
+        """)
+
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot(
+                    label="Clinical Assistant",
+                    show_copy_button=True,
+                    height=600,
+                    type="messages",
+                    avatar_images=(None, "https://i.imgur.com/6wX7Zb4.png"),
+                    render_markdown=True
+                )
+            with gr.Column(scale=1):
+                file_upload = gr.File(label="Upload Excel File", file_types=[".xlsx"], height=100)
+                analyze_btn = gr.Button("🧠 Analyze Patient History", variant="primary", elem_classes="primary")
+                report_output = gr.File(label="Download Report", visible=False, interactive=False)
+
+        chatbot_state = gr.State(value=[])
+
+        def update_ui(file, current_state):
+            messages, report_path = process_final_report(agent, file, current_state)
+            formatted_messages = []
+            for msg in messages:
+                role = msg.get("role")
+                content = msg.get("content", "")
+                if role == "assistant":
+                    content = content.replace("- ", "\n- ")
+                    content = f"<div class='chat-message-content'>{content}</div>"
+                formatted_messages.append({"role": role, "content": content})
+            report_update = gr.update(visible=report_path is not None, value=report_path)
+            return formatted_messages, report_update, formatted_messages
+
+        analyze_btn.click(fn=update_ui, inputs=[file_upload, chatbot_state], outputs=[chatbot, report_output, chatbot_state], api_name="analyze")
 
     return demo
 
@@ -169,7 +338,7 @@ 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=True)
+        demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True, allowed_paths=["/data/hf_cache/reports"], share=False)
     except Exception as e:
         print(f"Error: {str(e)}")
-        sys.exit(1)
+        sys.exit(1)