Update app.py

app.py

@@ -1,64 +1,123 @@
 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, AutoTokenizer, AutoModelForSequenceClassification
+import random
+import torch
+import groq  # Assuming you are using the Groq library
+import os
+from dotenv import load_dotenv
+from huggingface_hub import login
+
+# Load environment variables from .env file
+load_dotenv()
+HUGGING_FACE_TOKEN = os.getenv("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")
+
+# Authenticate with Hugging Face (use your token)
+login(HUGGING_FACE_TOKEN)
+
+# Load the mental health counseling conversations dataset
+ds = load_dataset("Amod/mental_health_counseling_conversations")
+context = ds["train"]["Context"]
+response = ds["train"]["Response"]
+
+GROQ_API_KEY = "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"
+
+client = groq.Groq(api_key=GROQ_API_KEY)
+
+# Load FLAN-T5 model and tokenizer for primary RAG
+flan_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
+flan_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
+
+# Load sentiment analysis model
+sentiment_tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+sentiment_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
+
+# Groq client setup (assuming you have an API key)
+client = groq.Groq(api_key=GROQ_API_KEY)  # Corrected Groq client initialization
+# Function for sentiment analysis
+def analyze_sentiment(text):
+    inputs = sentiment_tokenizer(text, return_tensors="pt")
+    outputs = sentiment_model(**inputs)
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    sentiment = "positive" if torch.argmax(probs) == 1 else "negative"
+    confidence = probs.max().item()
+    return sentiment, confidence
+
+# Function to generate response based on sentiment and user input
+def generate_response(sentiment, user_input):
+    prompt = f"The user feels {sentiment}. Respond with supportive advice based on: {user_input}"
+    inputs = flan_tokenizer(prompt, return_tensors="pt")
+    response = flan_model.generate(**inputs, max_length=150)
+    return flan_tokenizer.decode(response[0], skip_special_tokens=True)
+# Main chatbot function
+def chatbot(user_input):
+    if not user_input.strip():
+        return "Please enter a question or concern to receive guidance."
 
+    # Word count limit
+    word_count = len(user_input.split())
+    max_words = 50
+    remaining_words = max_words - word_count
+    if remaining_words < 0:
+        return f"Your input is too long. Please limit it to {max_words} words."
+
+    # Sentiment analysis
+    sentiment, confidence = analyze_sentiment(user_input)
+
+    # Groq API fallback for a personalized response
+    try:
+        brief_response = client.chat.completions.create(
+            messages=[{
+                "role": "user",
+                "content": user_input,
+            }],
+            model="llama3-8b-8192",  # Change model if needed
+        )
+        brief_response = brief_response.choices[0].message.content
+    except Exception as e:
+        brief_response = None
+
+    if brief_response:
+        return f"**Personalized Response from Groq:** {brief_response}"
+
+    # Fallback to FLAN-T5 model for response generation
+    response_text = generate_response(sentiment, user_input)
+def generate_response(user_input):
+    # Generate response using FLAN-T5
+    inputs = flan_tokenizer.encode("summarize: " + user_input, return_tensors="pt", max_length=512, truncation=True)
+    summary_ids = flan_model.generate(inputs, max_length=100, num_beams=4, early_stopping=True)
+    generated_response = flan_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+    if not generated_response:
+        return "I'm sorry, I don't have information specific to your concern. Please consult a professional."
+
+    # Final response with different sources
+    complete_response = (
+        f"**Sentiment Analysis:** {sentiment} (Confidence: {confidence:.2f})\n\n"
+        f"**Generated Response:**\n{generated_response}\n\n"
+        f"**Contextual Information:**\n{context_text}\n\n"
+        f"**Additional Dataset Response:**\n{dataset_response}\n\n"
+        f"Words entered: {word_count}, Words remaining: {remaining_words}"
+    )
+
+    return complete_response
+
+# Example call to the function
+response = generate_response("This is an example input.")
+print(response)
+# Set up Gradio interface
+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"
+)
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app
+interface.launch()