Create app.py

app.py

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+import gradio as gr
+import pytube
+from youtube_transcript_api import YouTubeTranscriptApi as yt
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import os
+from langchain import PromptTemplate
+from langchain import LLMChain
+from langchain_together import Together
+import re
+import pdfplumber
+# Set the API key with double quotes
+
+os.environ['TOGETHER_API_KEY'] = "d88cb7414e4039a84d2ed63f1b47daaaa4230c4c53a422045d8a30a9a3bc87d8"
+
+text = ""
+max_pages = 16
+with pdfplumber.open("/content/New Data Set.pdf") as pdf:
+        for i, page in enumerate(pdf.pages):
+            if i >= max_pages:
+                break
+            text += page.extract_text() + "\n"
+
+def Bot(Questions):
+    chat_template = """
+    Based on the provided context: {text}
+    Please answer the following question: {Questions}
+
+    Only provide answers that are directly related to the context. If the question is unrelated, respond with "I don't know".
+    """
+    prompt = PromptTemplate(
+        input_variables=['text', 'Questions'],
+        template=chat_template
+    )
+    llama3 = Together(model="meta-llama/Llama-3-70b-chat-hf", max_tokens=50)
+    Generated_chat = LLMChain(llm=llama3, prompt=prompt)
+
+    try:
+        response = Generated_chat.invoke({
+            "text": text,
+            "Questions": Questions
+        })
+
+        response_text = response['text']
+
+        response_text = response_text.replace("assistant", "")
+
+        # Post-processing to handle repeated words and ensure completeness
+        words = response_text.split()
+        seen = set()
+        filtered_words = [word for word in words if word.lower() not in seen and not seen.add(word.lower())]
+        response_text = ' '.join(filtered_words)
+        response_text = response_text.strip()  # Ensuring no extra spaces at the ends
+        if not response_text.endswith('.'):
+            response_text += '.'
+
+        return response_text
+    except Exception as e:
+        return f"Error in generating response: {e}"
+
+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)
+    return response.translate(str.maketrans('', '', '\n'))
+  # text_embedding = model.encode(text, convert_to_tensor=True)
+  # statement_embedding = model.encode(statement, convert_to_tensor=True)
+
+  # # Compute the cosine similarity between the embeddings
+  # similarity = util.pytorch_cos_sim(text_embedding, statement_embedding)
+
+  # # Print the similarity score
+  # print(f"Cosine similarity: {similarity.item()}")
+
+  # # Define a threshold for considering the statement as related
+  # threshold = 0.7
+
+  # if similarity.item() > threshold:
+  #   response=Bot(Questions)
+  #   return response
+  # else:
+  #   response="The statement is not related to the text."
+  #   return response
+
+iface = gr.Interface(fn=ChatBot, inputs="text", outputs="text", title="Chatbot")
+iface.launch(debug=True)
+
+
+
+
+
+
+
+
+