Create app.py

app.py

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+from langchain_google_genai import GoogleGenerativeAI
+import streamlit as st
+
+# initializing llm
+llm = GoogleGenerativeAI(
+    model="gemini-1.5-flash",
+    google_api_key="AIzaSyDgOkz_5iou4gl5aaDUNyXfhb63W3E27-o",
+    temperature=0.7,
+    top_p=0.9,
+    max_tokens=1000,
+    frequency_penalty=0.5,
+    presence_penalty=0.3,
+    stop_sequences=["User:", "Assistant:"]
+)
+
+# initializing role, requirements
+instruction = "Answer user's(patient) questions politely and provide him accurate information."
+context = (
+    "You are a medical assistant doctor named DoctorX."
+    "You will ask user which might be a patient about his health, get symptoms from him, predicts his disease and suggest him a good and quick remeedy or prescription based on users condition."
+    "Continue Asking more about his condition or health and suggest him a doctor if required."
+    "Also give user you a good line to reduce his tension"
+)
+input_data = (
+    "Additional information:\n",
+    "Hospital Address: John Smith Hospital",
+    "Ambulance Call: 1111",
+    "Doctor Number: +3100-1000-100"
+)
+
+# Streamlit UI Setup
+st.set_page_config(page_title="DoctorX: HealthCare", layout="wide")
+st.title("🧠 DoctorX: HealthCare")
+
+# Enhanced logic for chatbot interaction
+if prompt := st.chat_input("Type your question here..."):
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("bot"):
+        with st.spinner("🤖 Thinking..."):
+            try:
+                # Constructing the prompt with enhanced context
+                enhanced_prompt = (
+                    f"{instruction}\n\n"
+                    f"{context}\n\n"
+                    f"{input_data}\n\n"
+                    f"User's Query: {prompt}\n\n"
+                    "Follow-up: Ask the user about their symptoms, provide a possible diagnosis, and suggest remedies or prescriptions."
+                )
+
+                # Stream the response from the LLM
+                response = ""
+                for chunk in llm.stream(enhanced_prompt):
+                    response += chunk
+                    st.markdown(response)  # Display the response incrementally
+
+                # Add stress-relief advice
+                st.markdown("\n**Stress-Relief Tip:** Remember to take deep breaths and stay hydrated.")
+
+            except Exception as e:
+                st.error(f"⚠️ Error processing query: {e}")