Update app.py

app.py

@@ -1,128 +1,3 @@
-# import os
-# import streamlit as st
-# import torch
-# from langchain.chains import LLMChain
-# from langchain.prompts import ChatPromptTemplate
-# from langchain_huggingface import HuggingFaceEndpoint
-
-# def create_conversation_prompt(name1: str, name2: str, persona_style: str):
-#     """
-#     Create a prompt that instructs the model to produce exactly 15 messages
-#     of conversation, alternating between name1 and name2, starting with name1.
-
-#     We will be very explicit and not allow any formatting except the required lines.
-#     """
-#     prompt_template_str = f"""
-#     You are simulating a conversation of exactly 15 messages between two people: {name1} and {name2}.
-#     {name1} speaks first (message 1), then {name2} (message 2), then {name1} (message 3), and so forth,
-#     alternating until all 15 messages are complete. The 15th message is by {name1}.
-
-#     Requirements:
-#     - Output exactly 15 lines, no more, no less.
-#     - Each line must be a single message in the format:
-#       {name1}: <message> or {name2}: <message>
-#     - Do not add any headings, numbers, sample outputs, or explanations.
-#     - Do not mention code, programming, or instructions.
-#     - Each message should be 1-2 short sentences, friendly, natural, reflecting the style: {persona_style}.
-#     - Use everyday language, can ask questions, show opinions.
-#     - Use emojis sparingly if it fits the style (no more than 1-2 total).
-#     - No repeated lines, each message should logically follow from the previous one.
-#     - Do not produce anything after the 15th message. No extra lines or text.
-
-#     Produce all 15 messages now:
-#     """
-#     return ChatPromptTemplate.from_template(prompt_template_str)
-
-# def create_summary_prompt(name1: str, name2: str, conversation: str):
-#     """Prompt for generating a title and summary."""
-#     summary_prompt_str = f"""
-#     Below is a completed 15-message conversation between {name1} and {name2}:
-
-#     {conversation}
-
-#     Please provide:
-#     Title: <A short descriptive title of the conversation>
-#     Summary: <A few short sentences highlighting the main points, tone, and conclusion>
-
-#     Do not continue the conversation, do not repeat it, and do not add extra formatting beyond the two lines:
-#     - One line starting with "Title:"
-#     - One line starting with "Summary:"
-#     """
-#     return ChatPromptTemplate.from_template(summary_prompt_str)
-
-# def main():
-#     st.title("LLM Conversation Simulation")
-
-#     model_names = [
-#         "meta-llama/Llama-3.3-70B-Instruct",
-#         "mistralai/Mistral-7B-v0.1",
-#         "tiiuae/falcon-7b"
-        
-#     ]
-#     selected_model = st.selectbox("Select a model:", model_names)
-
-#     name1 = st.text_input("Enter the first user's name:", value="Alice")
-#     name2 = st.text_input("Enter the second user's name:", value="Bob")
-#     persona_style = st.text_area("Enter the persona style characteristics:", 
-#                                  value="friendly, curious, and a bit sarcastic")
-
-#     if st.button("Start Conversation Simulation"):
-#         st.write("**Loading model...**")
-#         print("Loading model...")
-
-#         with st.spinner("Starting simulation..."):
-#             endpoint_url = f"https://api-inference.huggingface.co/models/{selected_model}"
-
-#             try:
-#                 llm = HuggingFaceEndpoint(
-#                     endpoint_url=endpoint_url,
-#                     huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN"),
-#                     task="text-generation",
-#                     temperature=0.7,
-#                     max_new_tokens=512
-#                 )
-#                 st.write("**Model loaded successfully!**")
-#                 print("Model loaded successfully!")
-#             except Exception as e:
-#                 st.error(f"Error initializing HuggingFaceEndpoint: {e}")
-#                 print(f"Error initializing HuggingFaceEndpoint: {e}")
-#                 return
-
-#             conversation_prompt = create_conversation_prompt(name1, name2, persona_style)
-#             conversation_chain = LLMChain(llm=llm, prompt=conversation_prompt)
-
-#             st.write("**Generating the full 15-message conversation...**")
-#             print("Generating the full 15-message conversation...")
-
-#             try:
-#                 # Generate all 15 messages in one go
-#                 conversation = conversation_chain.run(chat_history="", input="").strip()
-
-#                 st.subheader("Final Conversation:")
-#                 st.text(conversation)
-#                 print("Conversation Generation Complete.\n")
-#                 print("Full Conversation:\n", conversation)
-
-#                 # Summarize the conversation
-#                 summary_prompt = create_summary_prompt(name1, name2, conversation)
-#                 summary_chain = LLMChain(llm=llm, prompt=summary_prompt)
-
-#                 st.subheader("Summary and Title:")
-#                 st.write("**Summarizing the conversation...**")
-#                 print("Summarizing the conversation...")
-
-#                 summary = summary_chain.run(chat_history="", input="")
-#                 st.write(summary)
-#                 print("Summary:\n", summary)
-
-#             except Exception as e:
-#                 st.error(f"Error generating conversation: {e}")
-#                 print(f"Error generating conversation: {e}")
-
-# if __name__ == "__main__":
-#     main()
-
-
 import os
 import streamlit as st
 import torch
@@ -130,21 +5,18 @@ from langchain.chains import LLMChain
 from langchain.prompts import ChatPromptTemplate
 from langchain_huggingface import HuggingFaceEndpoint
 
-# Additional imports for AnimateDiff
-from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
-from diffusers.utils import export_to_gif
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-
 def create_conversation_prompt(name1: str, name2: str, persona_style: str):
     """
     Create a prompt that instructs the model to produce exactly 15 messages
     of conversation, alternating between name1 and name2, starting with name1.
+
+    We will be very explicit and not allow any formatting except the required lines.
     """
     prompt_template_str = f"""
     You are simulating a conversation of exactly 15 messages between two people: {name1} and {name2}.
     {name1} speaks first (message 1), then {name2} (message 2), then {name1} (message 3), and so forth,
     alternating until all 15 messages are complete. The 15th message is by {name1}.
+
     Requirements:
     - Output exactly 15 lines, no more, no less.
     - Each line must be a single message in the format:
@@ -156,6 +28,7 @@ def create_conversation_prompt(name1: str, name2: str, persona_style: str):
     - Use emojis sparingly if it fits the style (no more than 1-2 total).
     - No repeated lines, each message should logically follow from the previous one.
     - Do not produce anything after the 15th message. No extra lines or text.
+
     Produce all 15 messages now:
     """
     return ChatPromptTemplate.from_template(prompt_template_str)
@@ -164,10 +37,13 @@ def create_summary_prompt(name1: str, name2: str, conversation: str):
     """Prompt for generating a title and summary."""
     summary_prompt_str = f"""
     Below is a completed 15-message conversation between {name1} and {name2}:
+
     {conversation}
+
     Please provide:
     Title: <A short descriptive title of the conversation>
     Summary: <A few short sentences highlighting the main points, tone, and conclusion>
+
     Do not continue the conversation, do not repeat it, and do not add extra formatting beyond the two lines:
     - One line starting with "Title:"
     - One line starting with "Summary:"
@@ -175,12 +51,14 @@ def create_summary_prompt(name1: str, name2: str, conversation: str):
     return ChatPromptTemplate.from_template(summary_prompt_str)
 
 def main():
-    st.title("LLM Conversation Simulation + AnimateDiff Video")
+    st.title("LLM Conversation Simulation")
 
     model_names = [
         "meta-llama/Llama-3.3-70B-Instruct",
         "mistralai/Mistral-7B-v0.1",
-        "tiiuae/falcon-7b"
+        "tiiuae/falcon-7b",
+        "EleutherAI/gpt-neox-20b"
+        
     ]
     selected_model = st.selectbox("Select a model:", model_names)
 
@@ -238,59 +116,9 @@ def main():
                 st.write(summary)
                 print("Summary:\n", summary)
 
-                # Extract the summary line from the summary text
-                lines = summary.split("\n")
-                summary_line = ""
-                for line in lines:
-                    if line.strip().lower().startswith("summary:"):
-                        summary_line = line.split("Summary:", 1)[-1].strip()
-                        break
-                if not summary_line:
-                    summary_line = "A friendly scene reflecting the conversation."
-
-                # Now integrate AnimateDiff for text-to-video generation
-                st.write("**Generating animation from summary using ByteDance/AnimateDiff-Lightning...**")
-                print("Generating animation from summary using ByteDance/AnimateDiff-Lightning...")
-
-                device = "cuda" if torch.cuda.is_available() else "cpu"
-                dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-                step = 4  # Adjust if needed
-                repo = "ByteDance/AnimateDiff-Lightning"
-                ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-                base = "emilianJR/epiCRealism"  # Check if this model exists or choose a known base model
-
-                # Load and configure AnimateDiff pipeline
-                adapter = MotionAdapter().to(device, dtype)
-                adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))
-
-                pipe = AnimateDiffPipeline.from_pretrained(
-                    base,
-                    motion_adapter=adapter,
-                    torch_dtype=dtype
-                ).to(device)
-
-                pipe.scheduler = EulerDiscreteScheduler.from_config(
-                    pipe.scheduler.config,
-                    timestep_spacing="trailing",
-                    beta_schedule="linear"
-                )
-
-                # Generate the animation
-                output = pipe(prompt=summary_line, guidance_scale=1.0, num_inference_steps=step)
-
-                # Save as GIF
-                # output.frames is a list of frames (PIL images)
-                st.write("**Exporting animation to GIF...**")
-                print("Exporting animation to GIF...")
-                export_to_gif(output.frames, "animation.gif")
-
-                st.subheader("Generated Animation:")
-                st.image("animation.gif", caption="Generated by AnimateDiff using summary prompt")
-
             except Exception as e:
-                st.error(f"Error generating conversation or summary: {e}")
-                print(f"Error generating conversation or summary: {e}")
+                st.error(f"Error generating conversation: {e}")
+                print(f"Error generating conversation: {e}")
 
 if __name__ == "__main__":
     main()