Update app.py

app.py

@@ -11,7 +11,6 @@ import os
 import requests
 import pickle
 import numpy as np
-import google.generativeai as genai
 
 # Load model once
 with open("best_clf.pkl", "rb") as file:
@@ -24,11 +23,73 @@ try:
     load_dotenv()
 except:
     pass
-GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
-
-genai.configure(api_key=GOOGLE_API_KEY)
 
 # Get the token from environment variables
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+
+def query_huggingface_model(selected_model: dict, input_data, input_type="text",max_tokens=512,task="text-classification",temperature=0.7, top_p=0.9 ):
+    API_URL = selected_model.get("url")
+    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+    try:
+        if input_type == "text":
+            if task == "text-generation":
+                payload = {
+                "messages": [
+                        {
+                            "role": "user",
+                            "content": input_data
+                        }
+                    ],                                       
+                    "model":selected_model.get("model")
+                }
+                    
+            else:
+                payload = {
+                    "inputs": input_data ,
+                  
+                    }
+            response = requests.post(API_URL, headers=headers, json=payload)
+
+        elif input_type == "image":
+            with open(input_data, "rb") as f:
+                data = f.read()
+                response = requests.post(API_URL, headers=headers, data=data)
+
+        else:
+            return {"error": f"Unsupported input_type: {input_type}"}
+
+        response.raise_for_status()
+        return response.json()
+
+    except requests.exceptions.RequestException as e:
+        return {"error": str(e)}
+def extract_response_content(response):
+    print(f"Response is: {response}")
+    
+    # For text generation or image captioning
+    if isinstance(response, list):
+        if response and isinstance(response[0], dict) and "generated_text" in response[0]:
+            return response[0]["generated_text"]
+
+        elif response and isinstance(response[0], list) and "label" in response[0][0]:
+            # For text classification
+            return [(item["label"], round(item["score"], 3)) for item in response[0]]
+    
+    # For OpenAI-style chat responses
+    elif isinstance(response, dict):
+        if "choices" in response and isinstance(response["choices"], list):
+            try:
+                return response["choices"][0]["message"]["content"]
+            except (KeyError, IndexError, TypeError):
+                return "Error: Could not extract message from choices"
+
+        elif "error" in response:
+            return f"Error: {response['error']}"
+
+    return "Unknown response format"
+
 # --- Step 1 ---
 if 'name' not in st.session_state:
     st.session_state.name = "Ange"
@@ -1030,10 +1091,17 @@ with st.container(key = "main"):
                     st.rerun()
 
         elif st.session_state.form5 == "next" :
-            model = genai.GenerativeModel('gemini-2.0-flash')
-            with st.container(key = "expert"):
-                with st.spinner("Model is Analysing your Results..."):
-                    response = model.generate_content(f"""
+            def generate_stream_response(text):
+                        # Yield the string one character at a time (for streaming)
+                        for char in text:
+                            yield char
+                            time.sleep(0.02)
+            selected_model = {
+                "url": "https://router.huggingface.co/nebius/v1/chat/completions",  # Replace with the Hugging Face API URL for your model
+                "model": "deepseek-ai/DeepSeek-V3"  # Replace with the model name
+            }
+            task = "text-generation"
+            prompt = f"""
                 Hi! A person named {st.session_state.name} has just been assessed for heart disease risk.
 
                 🔍 **Prediction**: {"High Risk" if st.session_state.Risk == 1 else "Low Risk"}
@@ -1054,7 +1122,13 @@ with st.container(key = "main"):
                 - Glucose: {st.session_state.glucose} mg/dL
 
                 💬 Please give a personalized, kind, and easy-to-understand explanation of this result. Include practical lifestyle advice and possible early warning signs to watch out for. Use an encouraging, empathetic tone.
-                """)
+                """
+                
+            with st.container(key = "expert"):
+                with st.spinner("Model is Analysing your Results..."):
+                    result = query_huggingface_model(selected_model, prompt , input_type="text",task=task)
+                    response = extract_response_content(result)
+                    
                 st.markdown(f"""
                 <div style="
                     font-size: 18px;
@@ -1077,7 +1151,7 @@ with st.container(key = "main"):
                 </div>
             """, unsafe_allow_html=True)
 
-                st.write(response.text)
+                st.write_stream(generate_stream_response(response))  # This will stream the text one character at a time