Update app.py

app.py

@@ -44,38 +44,7 @@ def retrieve_docs(query, k=5):
     retrieved_docs['distance'] = distances[0]
     return retrieved_docs
 
-# Parse response into structured sections
-def parse_response(response_text):
-    sections = {
-        "Symptoms": [],
-        "Signs": [],
-        "Risk Factors": [],
-        "Diagnostic Criteria": [],
-        "Other": []
-    }
-    
-    # Simple regex-based parsing (adjust based on your Gemini output format)
-    lines = response_text.split('\n')
-    current_section = "Other"
-    
-    for line in lines:
-        line = line.strip()
-        if line.lower().startswith("symptoms:"):
-            current_section = "Symptoms"
-        elif line.lower().startswith("signs:"):
-            current_section = "Signs"
-        elif line.lower().startswith("risk factors") or line.lower().startswith("past medical history:"):
-            current_section = "Risk Factors"
-        elif line.lower().startswith("diagnostic criteria:"):
-            current_section = "Diagnostic Criteria"
-        elif line and not line.startswith((' ', '\t')) and ':' in line:
-            current_section = "Other"
-        if line and not line.endswith(':'):
-            sections[current_section].append(line)
-    
-    return sections
-
-# Respond function with generic HTML formatting
+# Simplified respond function (no history)
 def respond(message, system_message, max_tokens, temperature, top_p):
     # Preprocess the user message
     preprocessed_query = preprocess_text(message)
@@ -89,8 +58,7 @@ def respond(message, system_message, max_tokens, temperature, top_p):
     prompt += (
         f"Query: {message}\n"
         f"Relevant Context: {context}\n"
-        f"Generate a concise response to the query based only on the provided context. "
-        f"Structure the response with clear sections like 'Symptoms:', 'Signs:', 'Risk Factors:', and 'Diagnostic Criteria:' where applicable."
+        f"Generate a short, concise, and to-the-point response to the query based only on the provided context."
     )
     
     # Generate response with Gemini
@@ -102,44 +70,20 @@ def respond(message, system_message, max_tokens, temperature, top_p):
         )
     )
     answer = response.text.strip()
+    if not answer.endswith('.'):
+        last_period = answer.rfind('.')
+        if last_period != -1:
+            answer = answer[:last_period + 1]
+        else:
+            answer += "."
+    
+    return answer
 
-    # Parse the response into sections
-    sections = parse_response(answer)
-
-    # Format the response into HTML with CSS styling
-    html_response = """
-    <style>
-        .diagnosis-container { font-family: Arial, sans-serif; line-height: 1.6; padding: 15px; max-width: 800px; margin: auto; }
-        h2 { color: #2c3e50; font-size: 24px; margin-bottom: 15px; border-bottom: 2px solid #2980b9; padding-bottom: 5px; }
-        h3 { color: #2980b9; font-size: 18px; margin-top: 15px; margin-bottom: 8px; }
-        ul { margin: 0; padding-left: 25px; }
-        li { margin-bottom: 6px; color: #34495e; }
-        p { margin: 5px 0; color: #34495e; }
-    </style>
-    <div class="diagnosis-container">
-        <h2>AI Response</h2>
-        <p>Based on the provided context, here is the information relevant to your query:</p>
-    """
-
-    # Add sections dynamically
-    for section, items in sections.items():
-        if items:  # Only include sections that have content
-            html_response += f"<h3>{section}</h3>"
-            html_response += "<ul>"
-            for item in items:
-                # Remove section prefix if present (e.g., "Symptoms:" from the first line)
-                cleaned_item = re.sub(rf"^{section}:", "", item, flags=re.IGNORECASE).strip()
-                html_response += f"<li>{cleaned_item}</li>"
-            html_response += "</ul>"
-
-    html_response += "</div>"
-    return html_response
-
-# Simple Gradio Interface with HTML output
+# Simple Gradio Interface
 demo = gr.Interface(
     fn=respond,
     inputs=[
-        gr.Textbox(label="Your Query", placeholder="Enter your medical question here (e.g., diabetes, heart failure)..."),
+        gr.Textbox(label="Your Query", placeholder="Enter your medical question here..."),
         gr.Textbox(
             value="You are a medical AI assistant diagnosing patients based on their query, using relevant context from past records of other patients.",
             label="System Message"
@@ -154,9 +98,9 @@ demo = gr.Interface(
             label="Top-p (nucleus sampling)",  # Included but not used by Gemini
         ),
     ],
-    outputs=gr.HTML(label="Diagnosis"),
-    title="🏥 Medical Query Assistant",
-    description="A medical assistant that diagnoses patient queries using AI and past records, with styled output for any condition."
+    outputs=gr.Textbox(label="Diagnosis"),
+    title="🏥 Medical Assistant",
+    description="A simple medical assistant that diagnoses patient queries using AI and past records."
 )
 
 if __name__ == "__main__":