Update app.py

app.py

@@ -4,10 +4,13 @@ from fpdf import FPDF
 import os
 from groq import Groq
 from deep_translator import GoogleTranslator
+import requests
 
 # Load API Key
 groq_api_key = os.getenv("groq_api_key")
 groq_client = Groq(api_key=groq_api_key)
+GROQ_API_KEY = os.getenv("groq_api_key")
+API_URL = "https://api.groq.com/v1/chat/completions"
 
 # Load Zero-Shot Classification Model
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -53,7 +56,6 @@ def translate_text(text, target_lang="en"):
 # Function to generate expert analysis
 def generate_expert_analysis(condition, symptoms):
     specialist_title = specialist_mapping.get(condition, ("General Physician", "General Medicine"))[0]
-
     prompt = f"""As a {specialist_title.lower()}, explain {condition} to a patient experiencing these symptoms: "{symptoms}".
     
     Structure the response into:
@@ -64,14 +66,12 @@ def generate_expert_analysis(condition, symptoms):
     5. **Diet Plan**  
     Use professional yet simple language. **No AI disclaimers or generic advice**.
     """
-
     response = groq_client.chat.completions.create(
         messages=[{"role": "user", "content": prompt}],
         model="mixtral-8x7b-32768",
         temperature=0.5,
         max_tokens=1024
     )
-
     return response.choices[0].message.content
 
 # Function to create a medical report
@@ -80,9 +80,7 @@ def create_medical_report(symptoms):
     result = classifier(translated_symptoms, conditions, multi_label=False)
     diagnosis = result['labels'][0]
     specialist, system = specialist_mapping.get(diagnosis, ("General Physician", "General Medicine"))
-
     expert_analysis = generate_expert_analysis(diagnosis, translated_symptoms)
-
     full_report = (
         f"**Medical Report**\n\n"
         f"**Patient Symptoms:** {translated_symptoms}\n"
@@ -96,50 +94,42 @@ def create_medical_report(symptoms):
         "3. What lifestyle changes help manage this condition?\n"
         "4. What warning signs require immediate attention?\n"
     )
-
     pdf = FPDF()
     pdf.add_page()
     pdf.set_font("Arial", size=12)
     pdf.multi_cell(0, 10, full_report)
     pdf_path = "medical_report.pdf"
     pdf.output(pdf_path)
-
     return full_report, pdf_path
 
-# Gradio UI Setup
-def show_section(selected):
-    return (
-        gr.update(visible=selected == "Home"),
-        gr.update(visible=selected == "Diagnosis")
-    )
-
-with gr.Blocks() as interface:
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("## 🏥 MedExpert")
-            gr.Markdown("AI-powered medical diagnosis system.")
-            nav_buttons = gr.Radio(["Home", "Diagnosis"], value="Home")
-
-        with gr.Column(scale=4):
-            with gr.Group(visible=True) as home_section:
-                gr.Markdown("## 🌟 Welcome to MedExpert")
-                gr.Markdown("### Your Intelligent Health Companion")
-                gr.Markdown("""
-                - Symptom Analysis & Diagnosis  
-                - Mental Health Support  
-                - Medical Report Generation  
-                """)
-
-            with gr.Group(visible=False) as diagnostic_section:
-                gr.Markdown("## 🩺 Symptom Analyzer")
-                symptoms_input = gr.Textbox(label="Describe your symptoms", placeholder="e.g., headache, fever, nausea...")
-                analyze_btn = gr.Button("Analyze Symptoms", variant="primary")
-                report_output = gr.Textbox(label="Medical Report", interactive=False)
-                pdf_output = gr.File(label="Download Full Report", file_count="single")
-
-    # Actions
-    analyze_btn.click(create_medical_report, inputs=symptoms_input, outputs=[report_output, pdf_output])
-    nav_buttons.change(show_section, inputs=[nav_buttons], outputs=[home_section, diagnostic_section])
+# Chatbot Integration
+def chat_with_groq(prompt, model="llama3-8b-8192", temperature=0.7, max_tokens=150):
+    headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
+    data = {"model": model, "messages": [{"role": "user", "content": prompt}], "temperature": temperature, "max_tokens": max_tokens}
+    response = requests.post(API_URL, headers=headers, json=data)
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"].strip()
+    else:
+        return f"Error: {response.status_code}, {response.text}"
+
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    response = chat_with_groq(prompt=message, model="llama3-8b-8192", temperature=temperature, max_tokens=max_tokens)
+    yield response
+
+system_message = (
+    "You are a compassionate medical assistant and mental health guide. "
+    "Provide supportive responses but avoid direct diagnoses or prescriptions."
+)
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value=system_message, label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
+    ],
+)
 
 # Run Interface
-interface.launch()
+demo.launch()