Update app.py

app.py

@@ -1,22 +1,24 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import os
-from langchain import PromptTemplate, LLMChain
+from langchain import PromptTemplate
+from langchain import LLMChain
 from langchain_together import Together
+import re
 import pdfplumber
+# Set the API key with double quotes
 
-# Set the API key
 os.environ['TOGETHER_API_KEY'] = "c2f52626b97118b71c0c36f66eda4f5957c8fc475e760c3d72f98ba07d3ed3b5"
 
-def extract_text_from_pdf(pdf_file, max_pages=16):
-    text = ""
-    with pdfplumber.open(pdf_file) as pdf:
+text = ""
+max_pages = 7
+with pdfplumber.open("Diabetes Type 1.pdf") as pdf:
         for i, page in enumerate(pdf.pages):
             if i >= max_pages:
                 break
             text += page.extract_text() + "\n"
-    return text
 
-def Bot(text, question):
+def Bot(Questions):
     chat_template = """
     Based on the provided context: {text}
     Please answer the following question: {Questions}
@@ -32,7 +34,7 @@ def Bot(text, question):
     try:
         response = Generated_chat.invoke({
             "text": text,
-            "Questions": question
+            "Questions": Questions
         })
 
         response_text = response['text']
@@ -52,33 +54,34 @@ def Bot(text, question):
     except Exception as e:
         return f"Error in generating response: {e}"
 
-def ChatBot(history, document, question):
-    greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"]
-    question_lower = question.lower().strip()
-    if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings):
-        return history + [("User", question), ("Bot", "Hello! How can I assist you with the document today?")]
+def ChatBot(Questions):
+  greetings = ["hi", "hello", "hey", "greetings", "what's up", "howdy"]
+    # Check if the input question is a greeting
+  question_lower = Questions.lower().strip()
+  if question_lower in greetings or any(question_lower.startswith(greeting) for greeting in greetings):
+        return "Hello! How can I assist you with the document today?"
+  else:
+    response=Bot(Questions)
+    print(response)
+    return response.translate(str.maketrans('', '', '\n'))
+  # text_embedding = model.encode(text, convert_to_tensor=True)
+  # statement_embedding = model.encode(statement, convert_to_tensor=True)
 
-    # Extract text from the uploaded PDF document
-    text = extract_text_from_pdf(document)
-    
-    # Generate the bot response based on the question and extracted text
-    response = Bot(text, question)
-    
-    # Update chat history with the user's question and bot's response
-    history.append(("User", question))
-    history.append(("Bot", response))
-    
-    return history
+  # # Compute the cosine similarity between the embeddings
+  # similarity = util.pytorch_cos_sim(text_embedding, statement_embedding)
 
-# Set up the Gradio interface using Blocks
-with gr.Blocks() as iface:
-    chatbot = gr.Chatbot()
-    document = gr.File(label="Upload PDF Document", type="filepath")
-    question = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
+  # # Print the similarity score
+  # print(f"Cosine similarity: {similarity.item()}")
 
-    def respond(history, document, question):
-        return ChatBot(history, document, question)
+  # # Define a threshold for considering the statement as related
+  # threshold = 0.7
 
-    question.submit(respond, [chatbot, document, question], chatbot)
+  # if similarity.item() > threshold:
+  #   response=Bot(Questions)
+  #   return response
+  # else:
+  #   response="The statement is not related to the text."
+  #   return response
 
-iface.launch(debug=True)
+iface = gr.Interface(fn=ChatBot, inputs="text", outputs="text", title="Chatbot")
+iface.launch(debug=True)