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Update app.py
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app.py
CHANGED
@@ -476,7 +476,7 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
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client = InferenceClient(model, token=huggingface_token)
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logging.info("InferenceClient initialized")
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-
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if file_type == "excel":
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# Excel functionality
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system_instruction = """You are a highly specialized Python programmer with deep expertise in data analysis and visualization using Excel spreadsheets.
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@@ -496,7 +496,7 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
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{"role": "system", "content": system_instruction},
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{"role": "user", "content": f"Based on the following data extracted from Excel spreadsheets:\n{context}\n\nPlease provide the Python code needed to execute the following task: '{query}'. Ensure that the code is derived directly from the dataset. If a chart is requested, use the matplotlib library to generate the appropriate visualization."}
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]
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-
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elif file_type == "pdf":
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# PDF functionality
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embed = get_embeddings()
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@@ -519,15 +519,15 @@ def get_response_from_llama(query, model, selected_docs, file_type, num_calls=1,
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else:
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raise ValueError("Invalid file type. Use 'excel' or 'pdf'.")
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logging.info(f"Prepared messages: {messages}")
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-
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full_response = ""
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for i in range(num_calls):
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logging.info(f"Starting API call {i+1}/{num_calls}")
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try:
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for message in client.chat.
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messages=messages,
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max_tokens=
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temperature=temperature,
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stream=True,
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):
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@@ -858,7 +858,7 @@ demo = gr.ChatInterface(
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],
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title="AI-powered PDF Chat and Web Search Assistant",
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description="Chat with your PDFs or use web search to answer questions.",
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theme=gr.Theme.from_hub("
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css=css,
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examples=[
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["Tell me about the contents of the uploaded PDFs."],
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client = InferenceClient(model, token=huggingface_token)
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logging.info("InferenceClient initialized")
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+
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if file_type == "excel":
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# Excel functionality
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system_instruction = """You are a highly specialized Python programmer with deep expertise in data analysis and visualization using Excel spreadsheets.
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{"role": "system", "content": system_instruction},
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{"role": "user", "content": f"Based on the following data extracted from Excel spreadsheets:\n{context}\n\nPlease provide the Python code needed to execute the following task: '{query}'. Ensure that the code is derived directly from the dataset. If a chart is requested, use the matplotlib library to generate the appropriate visualization."}
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]
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+
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elif file_type == "pdf":
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# PDF functionality
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embed = get_embeddings()
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else:
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raise ValueError("Invalid file type. Use 'excel' or 'pdf'.")
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# logging.info(f"Prepared messages: {messages}")
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full_response = ""
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for i in range(num_calls):
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logging.info(f"Starting API call {i+1}/{num_calls}")
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try:
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for message in client.chat.completion(
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messages=messages,
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max_tokens=2048,
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temperature=temperature,
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stream=True,
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):
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],
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title="AI-powered PDF Chat and Web Search Assistant",
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description="Chat with your PDFs or use web search to answer questions.",
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theme=gr.Theme.from_hub("allenai/gradio-theme"),
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css=css,
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examples=[
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["Tell me about the contents of the uploaded PDFs."],
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