Update pages/Phase1.py

pages/Phase1.py

@@ -1,33 +1,23 @@
 import streamlit as st
 from huggingface_hub import InferenceClient
-import os
 import fitz  # PyMuPDF
+import os
 
 st.title("ChatGPT-like Chatbot")
 
 base_url = "https://api-inference.huggingface.co/models/"
-
 API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
 headers = {"Authorization": "Bearer " + str(API_KEY)}
 
 model_links = {
-    "Mistral-7B": base_url + "mistralai/Mistral-7B-Instruct-v0.2",
-    "Mistral-22B": base_url + "mistral-community/Mixtral-8x22B-v0.1",
+    "Mistral-7B": base_url + "mistralai/Mistral-7B-Instruct-v0.2"
 }
 
 model_info = {
     "Mistral-7B": {
-        'description': """The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
-            \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over  **7 billion parameters.** \n""",
-        'logo': 'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
-    "Zephyr-7B": {
-        'description': """The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
-         \nFrom Huggingface: \n\
-         Zephyr is a series of language models that are trained to act as helpful assistants. \
-         [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\
-         is the third model in the series, and is a fine-tuned version of google/gemma-7b \
-         that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
-        'logo': 'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'}
+        'description': "Good Model",
+        'logo': 'model.jpg'
+    }
 }
 
 def format_prompt(message, custom_instructions=None):
@@ -42,22 +32,31 @@ def reset_conversation():
     st.session_state.messages = []
     return None
 
-def read_pdf(file_path):
-    doc = fitz.open(file_path)
+def read_pdf(file):
+    pdf_document = fitz.open(file)
     text = ""
-    for page in doc:
+    for page_num in range(len(pdf_document)):
+        page = pdf_document[page_num]
         text += page.get_text()
     return text
 
 models = [key for key in model_links.keys()]
 
+# Create the sidebar with the dropdown for model selection
 selected_model = st.sidebar.selectbox("Select Model", models)
-temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
-st.sidebar.button('Reset Chat', on_click=reset_conversation)
-st.sidebar.write(f"You're now chatting with **{selected_model}**")
+
+# Create a temperature slider
+temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
+
+# Add reset button to clear conversation
+st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button
+
+# Create model description
+st.sidebar.write(f"You're now chatting with {selected_model}")
 st.sidebar.markdown(model_info[selected_model]['description'])
 st.sidebar.image(model_info[selected_model]['logo'])
-st.sidebar.markdown("*Generated content may be inaccurate or false.*")
+st.sidebar.markdown("Generated content may be inaccurate or false.")
+st.sidebar.markdown("\nLearn how to build this chatbot here.")
 
 if "prev_option" not in st.session_state:
     st.session_state.prev_option = selected_model
@@ -67,44 +66,56 @@ if st.session_state.prev_option != selected_model:
     st.session_state.prev_option = selected_model
     reset_conversation()
 
+# Pull in the model we want to use
 repo_id = model_links[selected_model]
+
 st.subheader(f'AI - {selected_model}')
+st.title(f'ChatBot Using {selected_model}')
 
+# Initialize chat history
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
+# Display chat messages from history on app rerun
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-pdf_path = st.file_uploader("Upload a PDF file", type="pdf")
-
-if pdf_path:
-    pdf_text = read_pdf(pdf_path)
+# Upload PDF
+uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+if uploaded_file is not None:
+    pdf_text = read_pdf(uploaded_file)
+    st.session_state.pdf_text = pdf_text
+    st.write("PDF content loaded successfully!")
 
+# Accept user input
 if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
+
     custom_instruction = "Act like a Human in conversation"
+    
+    if "pdf_text" in st.session_state:
+        prompt = f"{st.session_state.pdf_text}\n\nQuestion: {prompt}"
 
+    # Display user message in chat message container
     with st.chat_message("user"):
         st.markdown(prompt)
-        st.session_state.messages.append({"role": "user", "content": prompt})
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
 
-    formatted_prompt = format_prompt(prompt, custom_instruction)
+    formated_text = format_prompt(prompt, custom_instruction)
 
+    # Display assistant response in chat message container
     with st.chat_message("assistant"):
-        with st.spinner("Thinking..."):
-            client = InferenceClient(model=repo_id, token=API_KEY)
-
-            if pdf_path:
-                # If a PDF is uploaded, use its text for answering
-                formatted_prompt = f"{pdf_text}\n\n{formatted_prompt}"
-
-            response = client.text_generation(
-                formatted_prompt, 
-                max_length=500, 
-                temperature=temp_values
-            )
-            
-            response_content = response["generated_text"]
-            st.markdown(response_content)
-            st.session_state.messages.append({"role": "assistant", "content": response_content})
+        client = InferenceClient(
+            model=model_links[selected_model],
+            headers=headers)
+
+        output = client.text_generation(
+            formated_text,
+            temperature=temp_values,  # 0.5
+            max_new_tokens=3000,
+            stream=True
+        )
+
+        response = st.write_stream(output)
+    st.session_state.messages.append({"role": "assistant", "content": response})