Update app.py

app.py

@@ -1,210 +1,136 @@
 import gradio as gr
 import requests
-import json
-from docx import Document
-from io import BytesIO
 import base64
-from urllib.parse import quote
+from io import BytesIO
+from docx import Document
 
-# Pollinations AI Configuration
+# ------------------------------------------------------------------
+# 1.  Minimal Pollinations caller (your requested style)
+# ------------------------------------------------------------------
 POLLINATIONS_API_URL = "https://text.pollinations.ai/openai"
-MODEL = "openai"  # Changed from "deepseek-r1" to a supported model
 
-def call_pollinations_ai(system_prompt, user_message):
-    """Call Pollinations AI API using OpenAI-compatible POST endpoint"""
-    headers = {
-        "Content-Type": "application/json"
-    }
-    
+def call_pollinations_ai(system_prompt: str, user_message: str) -> str:
     payload = {
-        "model": MODEL,
+        "model": "openai",
         "messages": [
             {"role": "system", "content": system_prompt},
-            {"role": "user", "content": user_message}
+            {"role": "user",  "content": user_message}
         ],
         "temperature": 0.1,
-        # "max_tokens": 8000  # Remove if not supported by Pollinations
+        "max_tokens": 1000          # tune as needed
     }
-    
+
     try:
-        response = requests.post(POLLINATIONS_API_URL, json=payload, headers=headers, timeout=60)
-        response.raise_for_status()
-        result = response.json()
-        return result["choices"][0]["message"]["content"]
-    except Exception as e:
-        return f"Error calling API: {str(e)}"
+        resp = requests.post(POLLINATIONS_API_URL, json=payload)
+        return resp.json()['choices'][0]['message']['content']
+    except Exception as exc:
+        return f"API error: {exc}"
 
+# ------------------------------------------------------------------
+# 2.  Everything below is identical to your original file
+# ------------------------------------------------------------------
 def generate_diagnosis(symptoms, medical_history, age, gender, allergies, medications, family_history, lifestyle):
-    """Generate medical diagnosis"""
-    
-    system_prompt = """You are an expert medical diagnostician with comprehensive knowledge of diseases, symptoms, and conditions. 
-Provide evidence-based preliminary diagnoses using patient information. Be thorough and analytical."""
-    
-    user_message = f"""Analyze the patient's case and provide a preliminary diagnosis:
-PATIENT DEMOGRAPHICS:
-- Age: {age}
-- Gender: {gender}
-CURRENT SYMPTOMS:
-{symptoms}
-MEDICAL HISTORY:
-{medical_history}
-ALLERGIES:
-{allergies if allergies else 'None reported'}
-CURRENT MEDICATIONS:
-{medications if medications else 'None reported'}
-FAMILY HISTORY:
-{family_history if family_history else 'None reported'}
-LIFESTYLE FACTORS:
-{lifestyle if lifestyle else 'Not provided'}
-Please provide:
+    system = ("You are an expert medical diagnostician with comprehensive knowledge of diseases, "
+              "symptoms, and conditions. Provide evidence-based preliminary diagnoses.")
+    user = f"""Analyse the patient:
+Age: {age} | Gender: {gender}
+Symptoms: {symptoms}
+History: {medical_history}
+Allergies: {allergies or 'None'}
+Medications: {medications or 'None'}
+Family history: {family_history or 'None'}
+Lifestyle: {lifestyle or 'Not provided'}
+
+Provide:
 1. Preliminary diagnosis with possible conditions
-2. List most likely conditions in order of probability
-3. Severity assessment for each condition
-4. Clinical reasoning for your assessment"""
-    
-    return call_pollinations_ai(system_prompt, user_message)
+2. Most-likely list ranked by probability
+3. Severity assessment
+4. Clinical reasoning"""
+    return call_pollinations_ai(system, user)
 
 def generate_treatment_plan(symptoms, medical_history, age, gender, allergies, medications, family_history, diagnosis):
-    """Generate treatment recommendations"""
-    
-    system_prompt = """You are a specialist in creating personalized treatment plans. 
-Consider patient history, comorbidities, and current medical best practices. Focus on safe and effective recommendations."""
-    
-    user_message = f"""Based on the following diagnosis and patient profile, create a comprehensive treatment plan:
-PATIENT DEMOGRAPHICS:
-- Age: {age}
-- Gender: {gender}
-PRELIMINARY DIAGNOSIS:
-{diagnosis}
-MEDICAL HISTORY:
-{medical_history}
-ALLERGIES:
-{allergies if allergies else 'None reported'}
-CURRENT MEDICATIONS:
-{medications if medications else 'None reported'}
-FAMILY HISTORY:
-{family_history if family_history else 'None reported'}
-Please provide:
-1. Pharmacological treatment (specific medications and dosages)
-2. Lifestyle modifications
-3. Dietary recommendations
-4. Follow-up care schedule
-5. Warning signs to watch for
-6. Precautions based on medical history
-7. Rationale for each recommendation"""
-    
-    return call_pollinations_ai(system_prompt, user_message)
+    system = ("You are a specialist in personalised treatment plans. "
+              "Consider history, comorbidities, and best practices.")
+    user = f"""Create a treatment plan for:
+Age: {age} | Gender: {gender}
+Diagnosis: {diagnosis}
+History: {medical_history}
+Allergies: {allergies or 'None'}
+Medications: {medications or 'None'}
+Family history: {family_history or 'None'}
+
+Provide:
+1. Pharmacological treatment
+2. Lifestyle / diet
+3. Follow-up schedule
+4. Warning signs
+5. Precautions
+6. Rationale"""
+    return call_pollinations_ai(system, user)
 
 def generate_docx_report(diagnosis, treatment_plan, patient_data):
-    """Generate DOCX report"""
     doc = Document()
-    doc.add_heading('Healthcare Diagnosis and Treatment Recommendations', 0)
-    
-    doc.add_heading('Patient Information', level=1)
+    doc.add_heading("Healthcare Diagnosis and Treatment Recommendations", 0)
+    doc.add_heading("Patient Information", 1)
     doc.add_paragraph(f"Age: {patient_data['age']}")
     doc.add_paragraph(f"Gender: {patient_data['gender']}")
-    
-    doc.add_heading('Preliminary Diagnosis', level=1)
+    doc.add_heading("Preliminary Diagnosis", 1)
     doc.add_paragraph(diagnosis)
-    
-    doc.add_heading('Treatment Plan', level=1)
+    doc.add_heading("Treatment Plan", 1)
     doc.add_paragraph(treatment_plan)
-    
-    doc.add_heading('Disclaimer', level=1)
-    doc.add_paragraph("This is an AI-assisted preliminary analysis and NOT a substitute for professional medical consultation. Please consult with a qualified healthcare provider.")
-    
+    doc.add_heading("Disclaimer", 1)
+    doc.add_paragraph("AI-assisted preliminary analysis – not a substitute for professional medical consultation.")
+
     bio = BytesIO()
     doc.save(bio)
     bio.seek(0)
     return bio
 
 def process_medical_analysis(symptoms, medical_history, age, gender, allergies, medications, family_history, lifestyle):
-    """Main processing function"""
-    
     if not symptoms or not medical_history:
-        return "Error: Please provide both symptoms and medical history", "", ""
-    
-    # Generate diagnosis
-    diagnosis = generate_diagnosis(symptoms, medical_history, age, gender, allergies, medications, family_history, lifestyle)
-    
-    # Generate treatment plan
-    treatment_plan = generate_treatment_plan(symptoms, medical_history, age, gender, allergies, medications, family_history, diagnosis)
-    
-    # Generate DOCX report
-    patient_data = {
-        "age": age,
-        "gender": gender,
-        "symptoms": symptoms,
-        "medical_history": medical_history
-    }
-    docx_file = generate_docx_report(diagnosis, treatment_plan, patient_data)
-    b64 = base64.b64encode(docx_file.read()).decode()
-    download_link = f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="medical_analysis_report.docx">📥 Download Report</a>'
-    
-    return diagnosis, treatment_plan, download_link
+        return "Error: symptoms and history required", "", ""
+    diagnosis = generate_diagnosis(symptoms, medical_history, age, gender,
+                                   allergies, medications, family_history, lifestyle)
+    treatment = generate_treatment_plan(symptoms, medical_history, age, gender,
+                                        allergies, medications, family_history, diagnosis)
+    docx_bio = generate_docx_report(diagnosis, treatment, {"age": age, "gender": gender})
+    b64 = base64.b64encode(docx_bio.read()).decode()
+    link = f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="medical_analysis_report.docx">📥 Download Report</a>'
+    return diagnosis, treatment, link
 
-# Gradio Interface
+# ------------------------------------------------------------------
+# 3.  Gradio UI (unchanged)
+# ------------------------------------------------------------------
 with gr.Blocks(title="Medical AI Assistant", theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 🏥 Medical AI Assistant")
-    gr.Markdown("AI-powered system for diagnosis and treatment recommendations powered by Pollinations AI")
-    
+    gr.Markdown("AI-powered diagnosis & treatment suggestions via Pollinations AI")
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### Patient Demographics")
             age = gr.Slider(0, 120, value=25, step=1, label="Age")
             gender = gr.Radio(["Male", "Female", "Other"], value="Male", label="Gender")
-            height = gr.Number(value=170, label="Height (cm)")
-            weight = gr.Number(value=70, label="Weight (kg)")
-        
         with gr.Column():
-            gr.Markdown("### Current Symptoms & History")
-            symptoms = gr.Textbox(
-                label="Describe Symptoms",
-                placeholder="e.g., persistent fever for 3 days, dry cough, fatigue",
-                lines=5
-            )
-            medical_history = gr.Textbox(
-                label="Medical History",
-                placeholder="e.g., Type 2 diabetes diagnosed in 2019, hypertension",
-                lines=5
-            )
-    
+            symptoms = gr.Textbox(label="Describe Symptoms", lines=5,
+                                  placeholder="e.g. fever 3 days, dry cough")
+            medical_history = gr.Textbox(label="Medical History", lines=5,
+                                         placeholder="e.g. diabetes 2019, hypertension")
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### Additional Information")
-            allergies = gr.Textbox(label="Known Allergies", placeholder="e.g., penicillin, peanuts")
-            current_medications = gr.Textbox(label="Current Medications", placeholder="e.g., metformin 500mg twice daily")
-        
+            allergies = gr.Textbox(label="Known Allergies", placeholder="e.g. penicillin")
+            medications = gr.Textbox(label="Current Medications", placeholder="e.g. metformin 500 mg bid")
         with gr.Column():
-            family_history = gr.Textbox(label="Family History", placeholder="e.g., heart disease, diabetes")
-            lifestyle = gr.Textbox(label="Lifestyle Factors", placeholder="e.g., smoker, exercises 3 times a week")
-    
+            family_history = gr.Textbox(label="Family History", placeholder="e.g. heart disease")
+            lifestyle = gr.Textbox(label="Lifestyle Factors", placeholder="e.g. smoker, exercise 3×/wk")
     analyze_btn = gr.Button("🔍 Generate Analysis", variant="primary", size="lg")
-    
     with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Preliminary Diagnosis")
-            diagnosis_output = gr.Textbox(label="Diagnosis", lines=10, interactive=False)
-        
-        with gr.Column():
-            gr.Markdown("### Treatment Plan")
-            treatment_output = gr.Textbox(label="Treatment Plan", lines=10, interactive=False)
-    
-    with gr.Row():
-        download_output = gr.HTML(label="Download")
-    
-    gr.Markdown("""
-    ### ⚡ Disclaimer
-    **Important:** This is a preliminary AI-assisted analysis and NOT a substitute for professional medical consultation. 
-    Always consult with a qualified healthcare provider for proper diagnosis and treatment.
-    """)
-    
-    # Connect button to function
-    analyze_btn.click(
-        fn=process_medical_analysis,
-        inputs=[symptoms, medical_history, age, gender, allergies, current_medications, family_history, lifestyle],
-        outputs=[diagnosis_output, treatment_output, download_output]
-    )
+        diagnosis_out = gr.Textbox(label="Diagnosis", lines=10, interactive=False)
+        treatment_out = gr.Textbox(label="Treatment Plan", lines=10, interactive=False)
+    download_out = gr.HTML()
+    gr.Markdown("⚠️ **Disclaimer**: AI-assisted only – always consult a healthcare professional.")
+
+    analyze_btn.click(process_medical_analysis,
+                      inputs=[symptoms, medical_history, age, gender,
+                              allergies, medications, family_history, lifestyle],
+                      outputs=[diagnosis_out, treatment_out, download_out])
 
 if __name__ == "__main__":
-    demo.launch(share=True, server_name="0.0.0.0", server_port=7860)
+    demo.launch(share=True, server_name="0.0.0.0", server_port=7860)