Update app.py

app.py

@@ -1,46 +1,67 @@
-from huggingface_hub import InferenceClient
 import gradio as gr
+import google.generativeai as genai
+import os
+from pypdf import PdfReader
 
-client = InferenceClient()
-
-def respond(
-    prompt: str,
-    history,
-):
-    if not history:
-        history = [{"role": "system", "content": "You are a friendly chatbot"}]
-    history.append({"role": "user", "content": prompt})
-
-    yield history
-
-    response = {"role": "assistant", "content": ""}
-    for message in client.chat_completion(
-        history,
-        temperature=0.95,
-        top_p=0.9,
-        max_tokens=512,
-        stream=True,
-        model="HuggingFaceH4/zephyr-7b-beta"
-    ):
-        response["content"] += message.choices[0].delta.content or ""
-
-        yield history + [response]
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Chat with Hugging Face Zephyr 7b 🤗")
-    chatbot = gr.Chatbot(
-        label="Agent",
-        type="messages",
-        avatar_images=(
-            None,
-            "https://em-content.zobj.net/source/twitter/376/hugging-face_1f917.png",
-        ),
-    )
-    prompt = gr.Textbox(max_lines=1, label="Chat Message")
-    prompt.submit(respond, [prompt, chatbot], [chatbot])
-    prompt.submit(lambda: "", None, [prompt])
-
-
-if __name__ == "__main__":
-    demo.launch()
+# Reemplaza con tu clave de API de Gemini
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+if not GOOGLE_API_KEY:
+    raise ValueError("La variable de entorno GOOGLE_API_KEY no está definida.")
+
+genai.configure(api_key=GOOGLE_API_KEY)
+model_text = genai.GenerativeModel('gemini-pro')
+model_vision = genai.GenerativeModel('gemini-pro-vision')
+
+def read_text_file(filepath):
+    with open(filepath, 'r', encoding='utf-8') as f:
+        return f.read()
+
+def read_pdf_file(filepath):
+    text = ""
+    with open(filepath, 'rb') as f:
+        reader = PdfReader(f)
+        for page in reader.pages:
+            text += page.extract_text() + "\n"
+    return text
+
+def process_input(instruction, text_file, pdf_file, image_file):
+    prompt_parts = []
+
+    if instruction:
+        prompt_parts.append(instruction)
+
+    if text_file:
+        file_content = read_text_file(text_file.name)
+        prompt_parts.append(f"Contenido del archivo de texto:\n{file_content}")
+
+    if pdf_file:
+        file_content = read_pdf_file(pdf_file.name)
+        prompt_parts.append(f"Contenido del archivo PDF:\n{file_content}")
+
+    if image_file:
+        # Para el modelo vision, necesitas pasar la imagen como datos
+        prompt_parts.append(image_file)
+        response = model_vision.generate_content(prompt_parts)
+        return response.text
+
+    if prompt_parts:
+        prompt = "\n\n".join(prompt_parts)
+        response = model_text.generate_content(prompt)
+        return response.text
+    else:
+        return "Por favor, proporciona alguna instrucción o archivo."
+
+iface = gr.Interface(
+    fn=process_input,
+    inputs=[
+        gr.Textbox(label="Instrucciones"),
+        gr.File(label="Archivo de Texto (.txt)"),
+        gr.File(label="Archivo PDF (.pdf)"),
+        gr.Image(label="Imagen"),
+    ],
+    outputs=gr.Textbox(label="Respuesta de Gemini"),
+    title="Interactúa con Gemini",
+    description="Sube archivos de texto, PDF o imágenes y proporciona instrucciones para que Gemini los procese.",
+)
+
+iface.launch()