Update app.py

app.py

@@ -1,29 +1,41 @@
 import os
 import streamlit as st
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-import torch
-# 
-# Use the pipeline method
-pipe = pipeline("text-generation", model="RayyanAhmed9477/Health-Chatbot")
+from groq import Groq
 
-# Load the model directly
-model_name = "RayyanAhmed9477/Health-Chatbot"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Set your API key in the environment
+os.environ["GROQ_API_KEY"] = "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"
 
-# Streamlit app setup
-st.title("Healthcare Chatbot")
-st.write("Ask me anything related to healthcare!")
+# Initialize Groq client with the API key from the environment
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
 
+# Function to interact with Groq API and get responses
+def get_chat_response(user_message):
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": user_message,
+            }
+        ],
+        model="llama-3.3-70b-versatile",
+    )
+    return chat_completion.choices[0].message.content
+
+# Streamlit UI setup
+st.title("AI-based Healthcare Chatbot")
+st.write("Welcome to the Healthcare Chatbot! Ask me anything about health.")
+
+# Text input for user query
 user_input = st.text_input("Your Question:")
 
 if user_input:
-    # Using the pipeline method
-    response = pipe([{"role": "user", "content": user_input}])
-    st.write(f"Response (using pipeline): {response[0]['generated_text']}")
+    # Get the response from Groq API
+    response = get_chat_response(user_input)
+    
+    # Display the chatbot's response
+    st.write("Chatbot Response:")
+    st.write(response)
 
-    # Using the model directly
-    inputs = tokenizer(user_input, return_tensors="pt")
-    outputs = model.generate(inputs["input_ids"], max_length=100, num_return_sequences=1)
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    st.write(f"Response (using direct model load): {generated_text}")
+# You can add more user-friendly features like history or options to rephrase responses, etc.