Update app.py

app.py

@@ -78,41 +78,41 @@ def load_sample_pdf():
     return "Sample PDF indexed successfully!"
 
 
-def format_docs(docs):
-    return "\n\n".join(doc.page_content for doc in docs)
+# def format_docs(docs):
+#     return "\n\n".join(doc.page_content for doc in docs)
     
 
-def generate_response(query, history, model, temperature, max_tokens, top_p, seed):
-    if vector_store is None:
-        return "Please upload and index a PDF at the Indexing tab."
+# def generate_response(query, history, model, temperature, max_tokens, top_p, seed):
+#     if vector_store is None:
+#         return "Please upload and index a PDF at the Indexing tab."
 
-    if seed == 0:
-        seed = random.randint(1, 100000)
+#     if seed == 0:
+#         seed = random.randint(1, 100000)
 
-    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
-    llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
-    custom_rag_prompt = PromptTemplate.from_template(template)
+#     retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 16})
+#     llm = ChatGroq(groq_api_key=os.environ.get("GROQ_API_KEY"), model=model)
+#     custom_rag_prompt = PromptTemplate.from_template(template)
    
-    rag_chain = (
-        {"context": retriever | format_docs, "question": RunnablePassthrough()}
-        | custom_rag_prompt
-        | llm
-        | StrOutputParser()
-    )
-
-    response = rag_chain.invoke(query)
+#     rag_chain = (
+#         {"context": retriever | format_docs, "question": RunnablePassthrough()}
+#         | custom_rag_prompt
+#         | llm
+#         | StrOutputParser()
+#     )
+
+#     response = rag_chain.invoke(query)
    
-    return response
+#     return response
 
 
 
-additional_inputs = [
-    gr.Dropdown(choices=["llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
-    gr.Slider(minimum=1, maximum=8000, step=1, value=8000, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
-    gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
-]
+# additional_inputs = [
+#     gr.Dropdown(choices=["llama-3.1-70b-versatile", "llama-3.1-8b-instant", "llama3-70b-8192", "llama3-8b-8192", "mixtral-8x7b-32768", "gemma2-9b-it", "gemma-7b-it"], value="llama-3.1-70b-versatile", label="Model"),
+#     gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Temperature", info="Controls diversity of the generated text. Lower is more deterministic, higher is more creative."),
+#     gr.Slider(minimum=1, maximum=8000, step=1, value=8000, label="Max Tokens", info="The maximum number of tokens that the model can process in a single response.<br>Maximums: 8k for gemma 7b it, gemma2 9b it, llama 7b & 70b, 32k for mixtral 8x7b, 132k for llama 3.1."),
+#     gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.5, label="Top P", info="A method of text generation where a model will only consider the most probable next tokens that make up the probability p."),
+#     gr.Number(precision=0, value=0, label="Seed", info="A starting point to initiate generation, use 0 for random")
+# ]
 
 # Create the Gradio interface
 with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
@@ -125,14 +125,14 @@ with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
         index_button.click(index_pdf, inputs=pdf_input, outputs=index_output)
         load_sample.click(load_sample_pdf, inputs=None, outputs=index_output)
     
-    with gr.Tab("Chatbot"):
-        gr.ChatInterface(
-            fn=generate_response, 
-            chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-            examples=examples_questions,
-            additional_inputs=additional_inputs,
-            cache_examples=False,
-        )       
+    # with gr.Tab("Chatbot"):
+    #     gr.ChatInterface(
+    #         fn=generate_response, 
+    #         chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    #         examples=examples_questions,
+    #         additional_inputs=additional_inputs,
+    #         cache_examples=False,
+    #     )       
 
 # Launch the Gradio app
 demo.launch()