Update app.py

app.py

@@ -45,39 +45,51 @@ st.markdown('<div class="blurred-background"></div>', unsafe_allow_html=True)
 
 #"""""""""""""""""""""""""   Application Code Starts here   """""""""""""""""""""""""""""""""""""""""""""
 
-# Hugging Face access token
-HF_TOKEN = "HF_TOKEN"  # Replace with your actual token or set it as an environment variable
+# Groq API Configuration
+api_key = os.environ.get("LawersGuideAPIKey")  # Ensure GROQ_API_KEY is set in your environment variables
+base_url = "https://api.groq.com/openai/v1/models/google/gemma-2-9b-it/completions"
 
-# Load the text generation pipeline with model and tokenizer
-@st.cache_resource
-def load_text_generation_pipeline():
-    model_name = "google/gemma-2-9b-it"
-    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HF_TOKEN)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name, 
-        load_in_8bit=True, 
-        device_map="auto", 
-        use_auth_token=HF_TOKEN
-    )
-    return pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-text_generator = load_text_generation_pipeline()
+headers = {
+    "Authorization": f"Bearer {api_key}",
+    "Content-Type": "application/json"
+}
 
-# Load the counseling dataset
+# Function to query Groq model
 @st.cache_resource
-def load_counseling_dataset():
-    return load_dataset("Amod/mental_health_counseling_conversations")
-
-dataset = load_counseling_dataset()
+def query_groq_model(prompt, max_tokens=100, temperature=0.7):
+    try:
+        payload = {
+            "prompt": prompt,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "top_p": 1.0,
+            "frequency_penalty": 0.0,
+            "presence_penalty": 0.0,
+            "n": 1
+        }
+        response = requests.post(base_url, headers=headers, json=payload)
+        response.raise_for_status()
+        result = response.json()
+        return result["choices"][0]["text"].strip()
+    except Exception as e:
+        return f"Error querying the model: {e}"
 
 # Streamlit App
 st.title("Mental Health Counseling Chat")
 st.markdown("""
 Welcome to the **Mental Health Counseling Chat Application**.  
-This platform is designed to provide **supportive, positive, and encouraging responses** using a fast and efficient language model.
+This platform is designed to provide **supportive, positive, and encouraging responses** using the Groq `google/gemma-2-9b-it` model.
 """)
 
-# Display example dataset entries
+# Load example dataset for user exploration (optional)
+@st.cache_resource
+def load_counseling_dataset():
+    from datasets import load_dataset
+    return load_dataset("Amod/mental_health_counseling_conversations")
+
+dataset = load_counseling_dataset()
+
+# Display example questions and answers from dataset
 if st.checkbox("Show Example Questions and Answers from Dataset"):
     sample = dataset["train"].shuffle(seed=42).select(range(3))  # Display 3 random samples
     for example in sample:
@@ -91,16 +103,13 @@ user_input = st.text_area("Your question or concern:", placeholder="Type your qu
 if st.button("Get Supportive Response"):
     if user_input.strip():
         try:
-            # Generate response using the text generation pipeline
+            # Query Groq model
             prompt = f"User: {user_input}\nCounselor:"
-            response = text_generator(prompt, max_length=100, num_return_sequences=1)
-            
-            # Extract and display the response
-            counselor_reply = response[0]["generated_text"].strip()
+            counselor_reply = query_groq_model(prompt, max_tokens=150, temperature=0.7)
             st.subheader("Counselor's Response:")
             st.write(counselor_reply)
         except Exception as e:
-            st.error(f"An error occurred while generating the response: {e}")
+            st.error(f"An error occurred while querying the model: {e}")
     else:
         st.error("Please enter a question or concern to receive a response.")