Update app.py

app.py

@@ -1,22 +1,139 @@
+# from huggingface_hub import InferenceClient
+# import gradio as gr
+
+# client = InferenceClient(
+#     "mistralai/Mixtral-8x7B-Instruct-v0.1"
+# )
+
+
+# def format_prompt(message, history):
+#   prompt = "<s>"
+#   for user_prompt, bot_response in history:
+#     prompt += f"[INST] {user_prompt} [/INST]"
+#     prompt += f" {bot_response}</s> "
+#   prompt += f"[INST] {message} [/INST]"
+#   return prompt
+
+# def generate(
+#     prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+# ):
+#     temperature = float(temperature)
+#     if temperature < 1e-2:
+#         temperature = 1e-2
+#     top_p = float(top_p)
+
+#     generate_kwargs = dict(
+#         temperature=temperature,
+#         max_new_tokens=max_new_tokens,
+#         top_p=top_p,
+#         repetition_penalty=repetition_penalty,
+#         do_sample=True,
+#         seed=42,
+#     )
+
+#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
+#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+#     output = ""
+
+#     for response in stream:
+#         output += response.token.text
+#         yield output
+#     return output
+
+
+# additional_inputs=[
+#     gr.Textbox(
+#         label="System Prompt",
+#         max_lines=1,
+#         interactive=True,
+#     ),
+#     gr.Slider(
+#         label="Temperature",
+#         value=0.9,
+#         minimum=0.0,
+#         maximum=1.0,
+#         step=0.05,
+#         interactive=True,
+#         info="Higher values produce more diverse outputs",
+#     ),
+#     gr.Slider(
+#         label="Max new tokens",
+#         value=256,
+#         minimum=0,
+#         maximum=1048,
+#         step=64,
+#         interactive=True,
+#         info="The maximum numbers of new tokens",
+#     ),
+#     gr.Slider(
+#         label="Top-p (nucleus sampling)",
+#         value=0.90,
+#         minimum=0.0,
+#         maximum=1,
+#         step=0.05,
+#         interactive=True,
+#         info="Higher values sample more low-probability tokens",
+#     ),
+#     gr.Slider(
+#         label="Repetition penalty",
+#         value=1.2,
+#         minimum=1.0,
+#         maximum=2.0,
+#         step=0.05,
+#         interactive=True,
+#         info="Penalize repeated tokens",
+#     )
+# ]
+
+# examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
+#           ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
+#           ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
+#           ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
+#           ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
+#           ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
+#          ]
+
+# gr.ChatInterface(
+#     fn=generate,
+#     chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+#     additional_inputs=additional_inputs,
+#     title="Mixtral 46.7B",
+#     examples=examples,
+#     concurrency_limit=20,
+# ).launch(show_api= True)
+
+
 from huggingface_hub import InferenceClient
 import gradio as gr
+import PyPDF2
 
 client = InferenceClient(
     "mistralai/Mixtral-8x7B-Instruct-v0.1"
 )
 
+def extract_text_from_pdf(file):
+    text = ""
+    with open(file.name, "rb") as f:
+        reader = PyPDF2.PdfFileReader(f)
+        for page_num in range(reader.numPages):
+            text += reader.getPage(page_num).extractText()
+    return text
 
 def format_prompt(message, history):
-  prompt = "<s>"
-  for user_prompt, bot_response in history:
-    prompt += f"[INST] {user_prompt} [/INST]"
-    prompt += f" {bot_response}</s> "
-  prompt += f"[INST] {message} [/INST]"
-  return prompt
+    prompt = "<s>"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
 
 def generate(
-    prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
+    prompt, history, system_prompt, pdf_file=None, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
 ):
+    if pdf_file is not None:
+        pdf_text = extract_text_from_pdf(pdf_file)
+        prompt += " " + pdf_text
+
     temperature = float(temperature)
     if temperature < 1e-2:
         temperature = 1e-2
@@ -40,7 +157,6 @@ def generate(
         yield output
     return output
 
-
 additional_inputs=[
     gr.Textbox(
         label="System Prompt",
@@ -82,7 +198,8 @@ additional_inputs=[
         step=0.05,
         interactive=True,
         info="Penalize repeated tokens",
-    )
+    ),
+    gr.File(label="Upload PDF Document", type="upload", max_size="100MB"),
 ]
 
 examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],