Create app.py

app.py

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+import os
+import speech_recognition as sr
+import fitz  # PyMuPDF
+from transformers import AutoTokenizer, AutoModel
+import torch
+import faiss
+import numpy as np
+from gtts import gTTS
+from pydub import AudioSegment
+from groq import Groq
+from dotenv import load_dotenv
+import gradio as gr
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Groq API client
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+# Initialize model and tokenizer for embedding
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+model = AutoModel.from_pretrained("bert-base-uncased")
+
+# Initialize vector database
+dimension = 768  # Size of BERT embeddings
+index = faiss.IndexFlatL2(dimension)
+
+# Folder path containing agriculture-related PDFs
+pdf_folder_path = "agriculture_pdfs"
+
+# Function to convert audio file to text
+def audio_to_text(audio_file_path):
+    recognizer = sr.Recognizer()
+    try:
+        with sr.AudioFile(audio_file_path) as source:
+            audio = recognizer.record(source)
+        text = recognizer.recognize_google(audio)
+        print(f"Extracted Text: {text}")  # Debugging line
+        return text
+    except sr.UnknownValueError:
+        print("Audio could not be understood")  # Debugging line
+        return None
+    except sr.RequestError:
+        print("Request error")  # Debugging line
+        return None
+
+# Function to convert audio to WAV format
+def convert_to_wav(audio_file_path):
+    if not audio_file_path:
+        raise ValueError("Invalid audio file path")
+    try:
+        audio = AudioSegment.from_file(audio_file_path)
+        wav_path = "temp_audio.wav"
+        audio.export(wav_path, format="wav")
+        return wav_path
+    except Exception as e:
+        print(f"Error converting audio to WAV: {e}")
+        return None
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    try:
+        pdf_document = fitz.open(pdf_file)
+        for page_num in range(len(pdf_document)):
+            page = pdf_document.load_page(page_num)
+            text += page.get_text()
+    except Exception as e:
+        print(f"Error extracting text from PDF: {e}")
+    return text
+
+# Function to embed text using a transformer model
+def embed_text(texts, model, tokenizer):
+    try:
+        inputs = tokenizer(texts, return_tensors='pt', truncation=True, padding=True)
+        with torch.no_grad():
+            embeddings = model(**inputs).last_hidden_state.mean(dim=1).numpy()
+        return embeddings
+    except Exception as e:
+        print(f"Error embedding text: {e}")
+        return np.array([])  # Return empty array on error
+
+# Function to convert text to speech
+def text_to_speech(text, output_file):
+    try:
+        tts = gTTS(text=text, lang='en')
+        tts.save(output_file)
+        return output_file
+    except Exception as e:
+        print(f"Error converting text to speech: {e}")
+        return None
+
+# Read all PDF files from the specified folder
+pdf_paths = [os.path.join(pdf_folder_path, f) for f in os.listdir(pdf_folder_path) if f.endswith('.pdf')]
+
+texts = []
+for path in pdf_paths:
+    pdf_text = extract_text_from_pdf(path)
+    if pdf_text:
+        texts.append(pdf_text)
+    else:
+        print(f"Failed to extract text from {path}")
+
+# Embed PDF texts and add to vector database
+embeddings = embed_text(texts, model, tokenizer)
+if embeddings.size > 0:
+    index.add(embeddings)
+else:
+    print("No embeddings to add to the vector database")
+
+def process_audio(audio_file):
+    if audio_file is None:
+        return "No audio file provided", None  # Handle case where no file is uploaded
+
+    if isinstance(audio_file, str):
+        audio_file_path = audio_file
+    else:
+        audio_file_path = audio_file.name
+
+    wav_path = convert_to_wav(audio_file_path)
+    if wav_path is None:
+        return "Error converting audio file to WAV format", None
+
+    text = audio_to_text(wav_path)
+    if not text:
+        return "No valid text extracted from audio", None
+
+    try:
+        audio_embedding = embed_text([text], model, tokenizer)[0]
+        if audio_embedding.size == 0:
+            return "Error generating embedding for the audio text", None
+        
+        distances, indices = index.search(np.array([audio_embedding]), k=2)
+        relevant_texts = [texts[idx] for idx in indices[0]]
+        combined_text = " ".join(relevant_texts)
+        if len(combined_text) > 1000:
+            combined_text = combined_text[:1000]
+
+        if not combined_text.strip():
+            return "No relevant information found in the PDFs", None
+
+        # Modify the prompt to reflect the agricultural domain
+        prompt = (
+            f"The user has provided the following agriculture-related query: {combined_text}. "
+            "Based on this, provide 4 clear and actionable steps to address the agricultural issue. "
+            "The response should be concise and no longer than 5 lines."
+        )
+
+        print(f"Prompt: {prompt}")  # Debugging line
+
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
+            ],
+            model="llama-3.1-70b-versatile",
+        )
+        response = chat_completion.choices[0].message.content
+        output_file = "agriculture_advice.mp3"
+        output_path = text_to_speech(response, output_file)
+
+        if output_path is None:
+            return "Error generating speech output", None
+
+        return response