Update app.py

app.py

@@ -1,11 +1,11 @@
 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")
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from gtts import gTTS
+import os
 
+# Load the AgriQBot model from Hugging Face using the transformers library
+tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/AgriQBot")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/AgriQBot")
 
 def respond(
     message,
@@ -15,50 +15,67 @@ def respond(
     temperature,
     top_p,
 ):
+    """
+    Respond to user queries using the AgriQBot model.
+    Args:
+    - message: User query (string).
+    - history: List of previous (user, assistant) message pairs.
+    - system_message: System-level instructions for the assistant.
+    - max_tokens: Maximum number of tokens in the response.
+    - temperature: Controls randomness in response.
+    - top_p: Controls diversity of the response.
+    
+    Returns:
+    - Response string as the chatbot's answer.
+    """
     messages = [{"role": "system", "content": system_message}]
 
+    # Construct the conversation history
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
+    # Append the current user message
     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
+    # Tokenize the input and generate the response
+    inputs = tokenizer(message, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(**inputs, max_length=max_tokens, temperature=temperature, top_p=top_p)
 
-        response += token
-        yield response
+    # Decode the response and return it
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
+def text_to_voice(response):
+    """
+    Convert the response text to speech using Google Text-to-Speech.
+    Args:
+    - response: Text response from the model to be converted to speech.
+    """
+    tts = gTTS(text=response, lang='en')
+    tts.save("response.mp3")
+    os.system("start response.mp3")  # Use 'open' for macOS, 'xdg-open' for Linux
 
-"""
-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"),
+# Build the Gradio Interface
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(value="You are a friendly farming assistant. Answer the user's questions related to farming.", label="System Message"),
+        gr.Textbox(label="Enter your question about farming:"),
+        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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
+    outputs=[
+        gr.Textbox(label="Chatbot Response"),
+        gr.Audio(value="response.mp3", label="Audio Response")
+    ],
+    title="Farming Assistant Chatbot",
+    description="Ask questions about farming, crop management, pest control, soil conditions, and best agricultural practices."
 )
 
-
+# Launch the interface
 if __name__ == "__main__":
     demo.launch()