Update ui/ui_core.py

ui/ui_core.py

@@ -1,47 +1,70 @@
+
+import sys
+import os
+
+# ✅ Add src to Python path
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))
+
+from txagent.txagent import TxAgent  # ✅ Now this will work
+import pandas as pd
+import pdfplumber
 import gradio as gr
-from PyPDF2 import PdfReader
-import docx
-
-def extract_text_from_uploaded_file(file_path):
-    if file_path.endswith(".pdf"):
-        try:
-            reader = PdfReader(file_path)
-            return "\n".join(page.extract_text() or "" for page in reader.pages)
-        except Exception as e:
-            return f"[Error extracting PDF text: {e}]"
-    elif file_path.endswith(".txt"):
-        try:
-            with open(file_path, "r", encoding="utf-8") as f:
-                return f.read()
-        except Exception as e:
-            return f"[Error reading text file: {e}]"
-    elif file_path.endswith(".docx"):
-        try:
-            doc = docx.Document(file_path)
-            return "\n".join([p.text for p in doc.paragraphs])
-        except Exception as e:
-            return f"[Error reading DOCX: {e}]"
-    else:
-        return "[Unsupported file type for text extraction]"
-
-def create_ui(agent):
+
+
+def extract_structured_text_from_csv(file_path):
+    try:
+        df = pd.read_csv(file_path)
+        relevant_columns = [
+            "Booking Number", "Form Name", "Form Item",
+            "Item Response", "Interviewer", "Interview Date"
+        ]
+        df = df[[col for col in relevant_columns if col in df.columns]]
+        return df.to_string(index=False)
+    except Exception as e:
+        return f"Error parsing CSV: {e}"
+
+
+def extract_structured_text_from_pdf(file_path):
+    extracted = []
+    try:
+        with pdfplumber.open(file_path) as pdf:
+            for page in pdf.pages:
+                tables = page.extract_tables()
+                for table in tables:
+                    for row in table:
+                        if any(row):
+                            extracted.append("\t".join([cell or "" for cell in row]))
+        return "\n".join(extracted)
+    except Exception as e:
+        return f"Error parsing PDF: {e}"
+
+
+def create_ui(agent: TxAgent):
     with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown("<h1 style='text-align: center;'>💊 TxAgent: Therapeutic Reasoning</h1>")
+        gr.Markdown("<h1 style='text-align: center;'>\ud83d\udc8a TxAgent: Therapeutic Reasoning</h1>")
         chatbot = gr.Chatbot(label="TxAgent", height=600, type="messages")
 
-        file_upload = gr.File(label="Upload Medical File", file_types=[".pdf", ".txt", ".docx"])
-        message_input = gr.Textbox(placeholder="Ask a biomedical question...", show_label=False)
+        file_upload = gr.File(label="Upload Medical File", file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv"], file_count="multiple")
+        message_input = gr.Textbox(placeholder="Ask a biomedical question or just upload the files...", show_label=False)
         send_button = gr.Button("Send", variant="primary")
         conversation_state = gr.State([])
 
-        def handle_chat(message, history, conversation, uploaded_file):
-            file_text = ""
-            if uploaded_file:
-                file_text = extract_text_from_uploaded_file(uploaded_file.name)
+        def handle_chat(message, history, conversation, uploaded_files):
+            context = (
+                "You are a clinical AI reviewing patient form data from interviews. "
+                "Your task is to analyze the responses, dates, and items, and reason step-by-step about "
+                "what the doctor might have overlooked. Do not summarize or answer yet — just reason step-by-step first."
+            )
 
-            # Append file text to the question
-            if file_text:
-                message += f"\n\n[Document Content Extracted from Upload]:\n{file_text}"
+            if uploaded_files:
+                extracted_text = ""
+                for file in uploaded_files:
+                    path = file.name
+                    if path.endswith(".csv"):
+                        extracted_text += extract_structured_text_from_csv(path) + "\n"
+                    elif path.endswith(".pdf"):
+                        extracted_text += extract_structured_text_from_pdf(path) + "\n"
+                message = f"{context}\n\n---\n{extracted_text.strip()}\n---\n\nNow reason what the doctor might have missed."
 
             generator = agent.run_gradio_chat(
                 message=message,
@@ -51,6 +74,7 @@ def create_ui(agent):
                 max_token=8192,
                 call_agent=False,
                 conversation=conversation,
+                uploaded_files=uploaded_files,
                 max_round=30
             )
             for update in generator:
@@ -60,11 +84,8 @@ def create_ui(agent):
         send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
         message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)
 
-        gr.Examples(
-            examples=[
-                ["Upload the files"],
-            ],
-            inputs=message_input,
-        )
+        gr.Examples([
+            ["Upload the files"],
+        ], inputs=message_input)
 
-    return demo
+    return demo