Update app.py

app.py

@@ -10,15 +10,11 @@ from langchain.vectorstores.chroma import Chroma
 from langchain.prompts import ChatPromptTemplate
 from langchain_community.llms.ollama import Ollama
 from get_embedding_function import get_embedding_function
-from langchain.document_loaders.pdf import PyPDFDirectoryLoader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain.schema.document import Document
+from langchain.document_loaders.pdf import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.schema import Document
 import tempfile
 
-# Create a temporary directory for Chroma if running in Hugging Face Spaces
-
-
-
 app = Flask(__name__)
 UPLOAD_FOLDER = '/code/uploads'
 CHROMA_PATH = tempfile.mkdtemp()  # Use the same folder for Chroma
@@ -54,21 +50,6 @@ Answer the question based only on the following context:
 Answer the question based on the above context: {question}
 """
 
-from bs4 import BeautifulSoup
-import requests
-from requests.auth import HTTPBasicAuth
-from PIL import Image
-from io import BytesIO
-import pandas as pd
-from urllib.parse import urlparse
-import os
-from pypdf import PdfReader
-from ai71 import AI71
-import uuid
-
-from inference_sdk import InferenceHTTPClient
-import base64
-
 AI71_API_KEY = os.environ.get('AI71_API_KEY')
 
 def generate_response(query, chat_history):
@@ -85,23 +66,6 @@ def generate_response(query, chat_history):
             response += chunk.choices[0].delta.content
     return response.replace("###", '').replace('\nUser:', '')
 
-def predict_pest(filepath):
-    CLIENT = InferenceHTTPClient(
-        api_url="https://detect.roboflow.com",
-        api_key="oF1aC4b1FBCDtK8CoKx7"
-    )
-    result = CLIENT.infer(filepath, model_id="pest-detection-ueoco/1")
-    return result['predictions'][0]
-    
-
-def predict_disease(filepath):
-    CLIENT = InferenceHTTPClient(
-        api_url="https://classify.roboflow.com",
-        api_key="oF1aC4b1FBCDtK8CoKx7"
-    )
-    result = CLIENT.infer(filepath, model_id="plant-disease-detection-iefbi/1")
-    return result['predicted_classes'][0]
-
 def convert_img(url, account_sid, auth_token):
     try:
         response = requests.get(url, auth=HTTPBasicAuth(account_sid, auth_token))
@@ -137,40 +101,6 @@ def get_weather(city):
     temperature = soup.find('div', attrs={'class': 'BNeawe iBp4i AP7Wnd'}).text
     return temperature
 
-from zenrows import ZenRowsClient
-Zenrow_api = os.environ.get('Zenrow_api')
-zenrows_client = ZenRowsClient(Zenrow_api)
-
-def get_rates():
-    url = "https://www.kisandeals.com/mandiprices/ALL/TAMIL-NADU/ALL"
-    response = zenrows_client.get(url)
-
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.content, 'html.parser')
-        rows = soup.select('table tbody tr')
-        data = {}
-        for row in rows:
-            columns = row.find_all('td')
-            if len(columns) >= 2:
-                commodity = columns[0].get_text(strip=True)
-                price = columns[1].get_text(strip=True)
-                if '₹' in price:
-                    data[commodity] = price
-    return str(data) + " These are the prices for 1 kg"
-
-def get_news():
-    news = []
-    url = "https://economictimes.indiatimes.com/news/economy/agriculture?from=mdr"
-    response = zenrows_client.get(url)
-
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.content, 'html.parser')
-        headlines = soup.find_all("div", class_="eachStory")
-        for story in headlines:
-            headline = story.find('h3').text.strip()
-            news.append(headline)
-    return news
-
 def download_and_save_as_txt(url, account_sid, auth_token):
     try:
         response = requests.get(url, auth=HTTPBasicAuth(account_sid, auth_token))
@@ -208,15 +138,7 @@ initialize_chroma()
 def query_rag(query_text: str):
     embedding_function = get_embedding_function()
     db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
-    print(query_text)
-    # Check if the query is related to a PDF
-    if "from pdf" in query_text.lower() or "in pdf" in query_text.lower():
-        # Provide some context about handling PDFs
-        response_text = "I see you're asking about a PDF-related query. Let me check the context from the PDF."
-    else:
-        # Regular RAG functionality
-        response_text = "Your query is not related to PDFs. Please make sure your question is clear."
-
+    
     results = db.similarity_search_with_score(query_text, k=5)
     
     if not results:
@@ -241,38 +163,19 @@ def query_rag(query_text: str):
         response_text = response.replace("###", '').replace('\nUser:', '')
 
     return response_text
-    
-def download_file(url, extension):
-    try:
-        response = requests.get(url)
-        response.raise_for_status()
-        filename = f"{uuid.uuid4()}{extension}"
-        file_path = os.path.join(UPLOAD_FOLDER, filename)
-        
-        with open(file_path, 'wb') as file:
-            file.write(response.content)
-        
-        print(f"File downloaded and saved as {file_path}")
-        return file_path
 
-    except requests.exceptions.HTTPError as err:
-        print(f"HTTP error occurred: {err}")
-    except Exception as err:
-        print(f"An error occurred: {err}")
-        return None
 def save_pdf_and_update_database(pdf_filepath):
     try:
-        document_loader = PyPDFDirectoryLoader(UPLOAD_FOLDER)
+        document_loader = PyPDFLoader(pdf_filepath)
         documents = document_loader.load()
-        
+
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=800,
             chunk_overlap=80,
             length_function=len,
-            is_separator_regex=False,
         )
         chunks = text_splitter.split_documents(documents)
-        
+
         add_to_chroma(chunks)
         print(f"PDF processed and data updated in Chroma.")
     except Exception as e:
@@ -294,6 +197,7 @@ def add_to_chroma(chunks: list[Document]):
             print(f"Chunks added to Chroma.")
     except Exception as e:
         print(f"Error adding chunks to Chroma: {e}")
+
 def calculate_chunk_ids(chunks):
     last_page_id = None
     current_chunk_index = 0
@@ -314,7 +218,6 @@ def calculate_chunk_ids(chunks):
 
     return chunks
 
-
 @app.route('/whatsapp', methods=['POST'])
 def whatsapp_webhook():
     incoming_msg = request.values.get('Body', '').lower()
@@ -331,74 +234,30 @@ def whatsapp_webhook():
             # Handle image processing (disease/pest detection)
             filepath = convert_img(media_url, account_sid, auth_token)
             response_text = handle_image(filepath)
-        else:
+        elif content_type == 'application/pdf':
             # Handle PDF processing
             filepath = download_and_save_as_txt(media_url, account_sid, auth_token)
-            response_text = process_and_query_pdf(filepath)
-    elif ('weather' in incoming_msg.lower()) or ('climate' in incoming_msg.lower()) or (
-            'temperature' in incoming_msg.lower()):
-        response_text = get_weather(incoming_msg.lower())
-    elif 'bookkeeping' in incoming_msg:
-        response_text = "Please provide the details you'd like to record."
-    elif ('rates' in incoming_msg.lower()) or ('price' in incoming_msg.lower()) or (
-            'market' in incoming_msg.lower()) or ('rate' in incoming_msg.lower()) or ('prices' in incoming_msg.lower()):
-        rates = get_rates()
-        response_text = generate_response(incoming_msg + ' data is ' + rates, chat_history)
-    elif ('news' in incoming_msg.lower()) or ('information' in incoming_msg.lower()):
-        news = get_news()
-        response_text = generate_response(incoming_msg + ' data is ' + str(news), chat_history)
+            save_pdf_and_update_database(filepath)
+            response_text = "PDF received and processed."
+        else:
+            response_text = "Unsupported media type. Please send a PDF or image file."
+    elif "weather" in incoming_msg:
+        city = incoming_msg.replace("weather", "").strip()
+        temperature = get_weather(city)
+        response_text = f"The current temperature in {city} is {temperature}"
     else:
+        # Generate response using the question and chat history
         response_text = query_rag(incoming_msg)
 
-    conversation_memory.add_to_memory({"user": incoming_msg, "assistant": response_text})
-    send_message(sender, response_text)
-    return '', 204
-
-def handle_image(filepath):
-    try:
-        disease = predict_disease(filepath)
-    except:
-        disease = None
-    try:
-        pest = predict_pest(filepath)
-    except:
-        pest = None
-
-    if disease:
-        response_text = f"Detected disease: {disease}"
-        disease_info = generate_response(f"Provide brief information about {disease} in plants", chat_history)
-        response_text += f"\n\nAdditional information: {disease_info}"
-    elif pest:
-        response_text = f"Detected pest: {pest}"
-        pest_info = generate_response(f"Provide brief information about {pest} in agriculture", chat_history)
-        response_text += f"\n\nAdditional information: {pest_info}"
-    else:
-        response_text = "Please upload another image with good quality."
-    
-    return response_text
-
-def process_and_query_pdf(filepath):
-    # Assuming the PDF processing and embedding are handled here.
-    add_to_chroma(load_documents())
-    return query_rag("from pdf")  # Replace with a more specific query if needed
+    # Add interaction to memory
+    interaction = {'role': 'user', 'content': incoming_msg, 'response': response_text}
+    conversation_memory.add_to_memory(interaction)
 
-
-def send_message(to, body):
-    try:
-        message = client.messages.create(
-            from_=from_whatsapp_number,
-            body=body,
-            to=to
-        )
-        print(f"Message sent with SID: {message.sid}")
-    except Exception as e:
-        print(f"Error sending message: {e}")
-        
-def send_initial_message(to_number):
-    send_message(
-        f'whatsapp:{to_number}',
-        'Welcome to the Agri AI Chatbot! How can I assist you today? You can send an image with "pest" or "disease" to classify it.'
-    )
+    # Send the response
+    resp = MessagingResponse()
+    msg = resp.message()
+    msg.body(response_text)
+    return str(resp)
 if __name__ == "__main__":
     send_initial_message('919080522395')
     send_initial_message('916382792828')