Update app.py

app.py

@@ -1,64 +1,83 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from datasets import load_dataset
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import random
+import groq  # Assuming you are using the Groq library
+
+# Load the dataset
+ds = load_dataset("Amod/mental_health_counseling_conversations")
+
+# Extract columns (updated to match dataset column names)
+context = ds["train"]["Context"]  # Column name is 'Context'
+response = ds["train"]["Response"]  # Column name is 'Response'
+
+# Load T5 model (small version)
+model_name = "t5-small"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Directly input the Groq API key (replace with your actual API key)
+api_key = "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"
+client = groq.Client(api_key=api_key)
+
+# Function to simulate conversation
+def chatbot(user_input):
+    if not user_input.strip():
+        return "Please enter a question or concern to receive guidance."
+
+    # Calculate the word count and remaining characters for the input
+    word_count = len(user_input.split())
+    max_words = 50  # Max words allowed for input
+    remaining_words = max_words - word_count
+
+    if remaining_words < 0:
+        return f"Your input is too long. Please limit to {max_words} words. Words remaining: 0."
+
+    # Try using the Groq API for the personalized response
+    try:
+        brief_response = client.predict(user_input)  # Make sure this method exists for your Groq client
+    except Exception as e:
+        brief_response = None  # If Groq fails, fall back to dataset
+
+    if brief_response:
+        return f"**Personalized Response:** {brief_response}"
+
+    # If Groq API does not work, fallback to dataset
+    idx = random.randint(0, len(context) - 1)
+    context_text = context[idx]
+    response_text = response[idx]
+
+    # Generate response using T5 (RAG approach)
+    inputs = tokenizer.encode("summarize: " + user_input, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = model.generate(inputs, max_length=100, num_beams=4, early_stopping=True)
+    generated_response = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+    if not generated_response:
+        return "Oops, sorry, I don't have information about your specific problem. Please visit a doctor to prevent mishaps."
+
+    # Final response combining generated answer and dataset info
+    complete_response = (
+        f"**Contextual Information:**\n{context_text}\n\n"
+        f"**Generated Response:**\n{generated_response}\n\n"
+        f"**Fallback Response:**\n{response_text}"
+    )
+
+    return f"{complete_response}\n\nWords entered: {word_count}, Words remaining: {remaining_words}"
+
+# Gradio interface setup
+interface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(
+        label="Ask your question:",
+        placeholder="Describe how you're feeling today...",
+        lines=4
+    ),
+    outputs=gr.Markdown(label="Psychologist Assistant Response"),
+    title="Virtual Psychiatrist Assistant",
+    description="Enter your mental health concerns, and receive guidance and responses from a trained assistant.",
+    theme="huggingface",  # Optional: apply a theme if available
 )
 
+# Launch the app
+interface.launch()
 
-if __name__ == "__main__":
-    demo.launch()