Create app.py

app.py

@@ -0,0 +1,77 @@
+import gradio as gr
+import os
+from langchain import PromptTemplate, LLMChain
+from langchain_together import Together
+import pdfplumber
+
+# Set the API key with double quotes
+os.environ['TOGETHER_API_KEY'] = "d88cb7414e4039a84d2ed63f1b47daaaa4230c4c53a422045d8a30a9a3bc87d8"
+
+def extract_text_from_pdf(pdf_file, max_pages=16):
+    text = ""
+    with pdfplumber.open(pdf_file) 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):
+    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": question
+        })
+
+        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(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?")]
+
+    text = extract_text_from_pdf(document)
+    response = Bot(text, question)
+    history.append(("User", question))
+    history.append(("Bot", response))
+    return history
+
+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...")
+
+    def respond(history, document, question):
+        return ChatBot(history, document, question)
+
+    question.submit(respond, [chatbot, document, question], chatbot)
+
+iface.launch(debug=True)