Update app.py

Modify the model...

app.py

@@ -1,58 +1,62 @@
 from langchain_community.document_loaders import PDFPlumberLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_core.prompts import PromptTemplate, FewShotPromptTemplate
+from langchain_core.prompts import PromptTemplate
 import gradio as gr
 import os
 from langchain_groq import ChatGroq
+from dotenv import load_dotenv
 
+
+MAX_DOC_LENGTH = 4000 
 def process_pdf(file):
     try:
-        loader = PDFPlumberLoader(file.name)
+        temp_path = file.name  
+        if not os.path.exists(temp_path):
+            return "Error: Uploaded file path does not exist."
+
+        loader = PDFPlumberLoader(temp_path)
         result = loader.load()
-        splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=20)
-        return splitter.split_documents(result)
-    except Exception as e:
-        return f"Error processing PDF: {str(e)}"
+        splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=20)
+        split_docs = splitter.split_documents(result)
 
+        # Extract text from the split documents
+        document_text = "\n".join([doc.page_content for doc in split_docs])
+        document_text = document_text[:MAX_DOC_LENGTH]
 
+        return document_text  # Returning the full document text
+    except Exception as e:
+        return f"Error processing PDF: {str(e)}"
 
 def initialize_llm():
-    groq_api_key = os.environ.get("GROQ_API_KEY")
+    load_dotenv()
+    groq_api_key = os.getenv("GROQ_API_KEY")
     if not groq_api_key:
         raise ValueError("GROQ_API_KEY environment variable not set.") 
 
     return ChatGroq(
-        model="mixtral-8x7b-32768",  
+        model= "llama3-8b-8192",  
         temperature=0.7,
         api_key=groq_api_key,
-        verbose = False
-    )
-def create_prompt():
-    examples = [
-        {"input": "What is the main topic discussed in the document?", 
-         "output": "The document discusses the concept and details of Neural Networks."},
-        {"input": "Explain the term 'activation function' as used in this document.", 
-         "output": "An activation function in the context of this document refers to a mathematical function applied to neurons' output to introduce non-linearity in the model."}
-    ]
-
-    example_template = PromptTemplate(
-        input_variables=["input", "output"],
-        template="Human: {input}\nAssistant: {output}"
+        verbose=False
     )
 
-    return FewShotPromptTemplate(
-        examples=examples,
-        example_prompt=example_template,
-        prefix="You are an AI assistant that provides specific and accurate answers based on the provided document.",
-        suffix="Human: {input}\nAssistant:",
-        input_variables=["input"]
+def create_prompt():
+    return PromptTemplate(
+        input_variables=["document", "question"],
+        template=(
+            "You are an AI assistant that provides precise answers based on the given document. "
+            "Use only the information available in the document to respond.\n\n"
+            "Document:\n{document}\n\n"
+            "Question: {question}\n"
+            "Answer:"
+        )
     )
 
-def generate_answer(chain, user_input):
+def generate_answer(chain, document_text, user_input):
     try:
-        response = chain.invoke({"input": user_input})
-        answer=response.content
-        return answer
+        response = chain.invoke({"document": document_text, "question": user_input})  
+        answer = response.content
+        return str(answer)
     except Exception as e:
         return f"Error generating answer: {str(e)}"
 
@@ -60,18 +64,18 @@ def handle_file(file, user_input):
     if not file:
         return "Please upload a PDF document."
     
-    data = process_pdf(file)
-    if isinstance(data, str): 
-        return data
+    document_text = process_pdf(file)
+    if isinstance(document_text, str) and document_text.startswith("Error"):
+        return document_text  # Return error message if processing failed
+
+    if not user_input.strip():
+        return "Please enter a question."
 
     llm = initialize_llm()
     prompt = create_prompt()
     chain = prompt | llm
 
-    if not user_input.strip():
-        return "Please enter a question."
-
-    return generate_answer(chain, user_input)
+    return generate_answer(chain, document_text, user_input)
 
 interface = gr.Interface(
     fn=handle_file,