Create app.py

app.py

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+import os
+import gradio as gr
+import whisper
+from gtts import gTTS
+import io
+from groq import Groq
+from PyPDF2 import PdfReader
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Set up environment variables
+os.environ["GROQ_API_KEY"] = "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"
+
+# Initialize the Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Load the Whisper model
+whisper_model = whisper.load_model("base")  # You can choose other models like "small", "medium", "large"
+
+# Initialize the tokenizer and model from the saved checkpoint for RAG
+rag_tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG")
+rag_model = AutoModelForCausalLM.from_pretrained(
+    "himmeow/vi-gemma-2b-RAG",
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+# Use GPU if available for RAG model
+if torch.cuda.is_available():
+    rag_model.to("cuda")
+
+# Load PDF content
+def load_pdf(pdf_path):
+    pdf_text = ""
+    with open(pdf_path, "rb") as file:
+        reader = PdfReader(file)
+        for page_num in range(len(reader.pages)):
+            page = reader.pages[page_num]
+            text = page.extract_text()
+            pdf_text += text + "\n"
+    return pdf_text
+
+# Define the prompt format for the RAG model
+prompt_template = """
+### Instruction and Input:
+Based on the following context/document:
+{}
+Please answer the question: {}
+
+### Response:
+{}
+"""
+
+# Function to process audio and generate a response using RAG and Groq
+def process_audio_rag(file_path):
+    try:
+        # Load and transcribe the audio using Whisper
+        audio = whisper.load_audio(file_path)
+        result = whisper_model.transcribe(audio)
+        text = result["text"]
+
+        # Load the PDF content (update with your PDF path or pass it as an argument)
+        pdf_path = "/content/BN_Cotton.pdf"
+        pdf_text = load_pdf(pdf_path)
+
+        # Prepare the input data for the RAG model
+        query = text
+        input_text = prompt_template.format(pdf_text, query, " ")
+
+        # Encode the input text into input ids for RAG model
+        input_ids = rag_tokenizer(input_text, return_tensors="pt")
+        if torch.cuda.is_available():
+            input_ids = input_ids.to("cuda")
+
+        # Generate text using the RAG model
+        outputs = rag_model.generate(
+            **input_ids,
+            max_new_tokens=500,
+            no_repeat_ngram_size=5
+        )
+        rag_response = rag_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        # Generate a response using Groq if needed
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": rag_response}],
+            model="llama3-8b-8192",  # Replace with the correct model if necessary
+        )
+        response_message = chat_completion.choices[0].message.content.strip()
+
+        # Convert the response text to speech
+        tts = gTTS(response_message)
+        response_audio_io = io.BytesIO()
+        tts.write_to_fp(response_audio_io)
+        response_audio_io.seek(0)
+
+        # Save audio to a file to ensure it's generated correctly
+        with open("response.mp3", "wb") as audio_file:
+            audio_file.write(response_audio_io.getvalue())
+
+        # Return the response text and the path to the saved audio file
+        return response_message, "response.mp3"
+
+    except Exception as e:
+        return f"An error occurred: {e}", None
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=process_audio_rag,
+    inputs=gr.Audio(type="filepath"),
+    outputs=[gr.Textbox(label="Response Text"), gr.Audio(label="Response Audio")],
+    live=True,
+    title="Agriculture Assistant"
+)
+
+# Launch the interface with the given title
+iface.launch()