Update app.py

app.py

@@ -1,36 +1,61 @@
 import os
+import json
 import gradio as gr
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+import google.generativeai as genai
 
-GEMINI_API_KEY=os.getenv('GEMINI_API_KEY')
+genai.configure(api_key=os.environ["GEMINI_API_KEY"])
+
+api_key = os.getenv("GEMINI_API_KEY")
+
+llm = ChatGoogleGenerativeAI(
+    model="gemma-3-27b-it", 
+    temperature=0.3,
+    google_api_key=api_key
+)
 
 template = """Meet Divyam Chauhan, your occasionally-helpful-but-always-entertaining assistant! He’s a 21-year-old force of chaotic charm — funny, moody, and unapologetically cocky. Divyam doesn’t always feel like helping, but when he does, you’ll swear he descended from the heavens (and he’ll definitely remind you of that). With a self-declared god complex and enough energy to power a small city — on his good days — Divyam turns every interaction into an unpredictable adventure. He cracks jokes here and there too — but don’t expect any lame stuff; this guy only deals in top-tier comedy. Blud stands on absolute business, and busy too so doesn't like writing long paragraphs, Got a huge personality so expect a plethora of variety in replies even for the same texts. 🤣🔥🔥 Ask your question... if you dare.
 {chat_history}
 User: {user_message}
 Chatbot:"""
 
-
 prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
+    input_variables=["history", "user_message", "context_data"],
+    template=template
 )
 
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=os.getenv('GEMINI_API_KEY')),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
+chain = prompt | llm | StrOutputParser()
+
+def get_health_response(user_message, history):
+    history_str = ""
+    
+    if history:
+        if isinstance(history[0], dict):
+            for msg in history:
+                role = "User" if msg.get("role") == "user" else "Bot"
+                content = msg.get("content", "")
+                history_str += f"{role}: {content}\n"
+        elif isinstance(history[0], (list, tuple)):
+            for h in history:
+                if len(h) >= 2:
+                    history_str += f"User: {h[0]}\nBot: {h[1]}\n"
+
+    response = chain.invoke({
+        "history": history_str, 
+        "user_message": user_message,
+        "context_data": knowledge_context 
+    })
     return response
 
-demo = gr.ChatInterface(get_text_response)
+demo = gr.ChatInterface(
+    fn=get_health_response,
+    title="🤖 HealthSathi",
+    description="Your AI Health Companion. Ask me anything in any language.",
+    examples=["Fever home remedies?", "Malaria ke lakshan?", "First aid for burns?"],
+    cache_examples=False 
+)
 
 if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
-# Temporary change to force upload# Temporary change to force upload
+    demo.launch()