Update app.py

Add code for Agent

app.py

@@ -1,64 +1,160 @@
 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)",
-        ),
-    ],
+import PyPDF2
+import os
+from openai import OpenAI
+import sys # Import sys to write to stderr
+
+# --- Configuration & Client Initialization ---
+# It's good practice to check for the API key at startup.
+# This provides a clear error if the secret isn't set in Hugging Face Spaces.
+NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")
+if not NEBIUS_API_KEY:
+    # Use a more descriptive error message.
+    raise ValueError("API Key not found. Please set the NEBIUS_API_KEY secret in your Hugging Face Space settings.")
+
+# Initialize the OpenAI client with your custom endpoint.
+# Ensure this base_url is correct and publicly accessible.
+client = OpenAI(
+    base_url="https://api.studio.nebius.com/v1/",
+    api_key=NEBIUS_API_KEY
 )
 
+# --- Core Functions ---
+
+def extract_text_from_pdf(pdf_file):
+    """Extracts text from an uploaded PDF file object."""
+    # pdf_file is a temporary file object from Gradio.
+    if not pdf_file:
+        return ""
+    try:
+        reader = PyPDF2.PdfReader(pdf_file.name)
+        text = ""
+        # Extract text from all pages except the first one.
+        for i, page in enumerate(reader.pages):
+            if i == 0:
+                continue
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
+        return text
+    except Exception as e:
+        print(f"Error reading PDF: {e}", file=sys.stderr)
+        return ""
+
+
+def get_llm_answer(pdf_text, question, history):
+    """
+    Sends the context, history, and question to the LLM and returns the answer.
+    """
+    # Truncate the PDF text to avoid exceeding the model's context limit.
+    context = pdf_text[:16000]
+
+    # The system prompt guides the model's behavior.
+    system_prompt = '''You are a helpful assistant who specializes in body composition, diet, and exercise.
+    Answer questions based on the provided document. Encourage the user to seek a professional
+    if they have serious concerns whenever appropriate.'''
+
+    # Construct the message payload for the API.
+    messages = [{"role": "system", "content": system_prompt}, 
+                {"role": "user", "content": f"Use the following document to answer my question:\n\n{context}"}, 
+                {"role":"user", "content": f"Question: {question}"}
+    ]
+
+    # Add the conversation history.
+    if history:
+        for msg in history:
+            if msg["role"] in ["user", "assistant"]:
+                messages.append(msg)
+    # Add the new user question
+    messages.append({"role": "user", "content": question})
+
+    try:
+        response = client.chat.completions.create(
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct",
+            temperature=0.6,
+            top_p=0.95,
+            messages=messages
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"Error calling OpenAI API: {e}", file=sys.stderr)
+        # Return a user-friendly error message.
+        return "Sorry, I encountered an error while trying to generate a response. Please check the logs."
+
+
+# --- Gradio Interface Logic ---
+
+# Use a class to manage state (the extracted PDF text).
+class PDFChatbot:
+    def __init__(self):
+        self.pdf_text = None
+        self.pdf_filename = None
+
+    def upload_pdf(self, pdf_file):
+        if pdf_file is None:
+            return "Status: No PDF uploaded."
+        
+        self.pdf_text = extract_text_from_pdf(pdf_file)
+        self.pdf_filename = os.path.basename(pdf_file.name)
+
+        if not self.pdf_text:
+            return f"Status: Could not extract text from {self.pdf_filename}. It might be empty, scanned, or protected."
+            
+        return f"Status: Successfully processed {self.pdf_filename}. You can now ask questions."
+
+    def chat(self, user_message, history):
+        if self.pdf_text is None:
+            # Add an instruction to the chatbot window if no PDF is uploaded.
+            #history.append([user_message, "Please upload a PDF document first."])
+            return "Please upload a PDF document first.", history
+        
+        if history is None:
+            history = []
+        context_history = [msg for msg in history if msg["role"] in ["user", "assistant"]]
+        # Get the answer from the LLM.
+        answer = get_llm_answer(self.pdf_text, user_message, context_history)
+        
+        # Append the user message and the assistant's answer to the history.
+        history = history + [
+            {"role": "user", "content": user_message }, 
+            {"role": "assistant", "content": answer }
+        ]
+        
+        # Return an empty string to clear the input textbox and the updated history.
+        return "", history
+
+# Instantiate the bot.
+pdf_bot = PDFChatbot()
+
+# Build the Gradio UI.
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# Body Composition Agent\nUpload a document about your body composition and ask questions about its content.")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            pdf_file = gr.File(label="Upload PDF", file_types=[".pdf"])
+            upload_btn = gr.Button("Process PDF", variant="primary")
+            upload_status = gr.Textbox(label="Status", interactive=False, value="Status: Waiting for PDF...")
+        
+        with gr.Column(scale=2):
+            chatbot = gr.Chatbot(type="messages", label="Chat History", height=500)
+            msg_textbox = gr.Textbox(label="Your Question:", interactive=True, placeholder="Type your question here...")
+            # Clear button is useful for starting a new conversation.
+            clear_btn = gr.ClearButton([msg_textbox, chatbot], value="Clear Chat")
+
+    # Wire up the event listeners.
+    upload_btn.click(
+        fn=pdf_bot.upload_pdf,
+        inputs=[pdf_file],
+        outputs=[upload_status]
+    )
+    
+    # Allow submitting questions with Enter key or the button.
+    msg_textbox.submit(
+        fn=pdf_bot.chat,
+        inputs=[msg_textbox, chatbot],
+        outputs=[msg_textbox, chatbot]
+    )
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app.
+demo.launch(debug=True) # Use debug=True to see errors in the console.